81 research outputs found
A Cloud Based Disaster Management System
The combination of wireless sensor networks (WSNs) and 3D virtual environments opens a new paradigm for their use in natural disaster management applications. It is important to have a realistic virtual environment based on datasets received from WSNs to prepare a backup rescue scenario with an acceptable response time. This paper describes a complete cloud-based system that collects data from wireless sensor nodes deployed in real environments and then builds a 3D environment in near real-time to reflect the incident detected by sensors (fire, gas leaking, etc.). The systemâs purpose is to be used as a training environment for a rescue team to develop various rescue plans before they are applied in real emergency situations. The proposed cloud architecture combines 3D data streaming and sensor data collection to build an efficient network infrastructure that meets the strict network latency requirements for 3D mobile disaster applications. As compared to other existing systems, the proposed system is truly complete. First, it collects data from sensor nodes and then transfers it using an enhanced Routing Protocol for Low-Power and Lossy Networks (RLP). A 3D modular visualizer with a dynamic game engine was also developed in the cloud for near-real time 3D rendering. This is an advantage for highly-complex rendering algorithms and less powerful devices. An Extensible Markup Language (XML) atomic action concept was used to inject 3D scene modifications into the game engine without stopping or restarting the engine. Finally, a multi-objective multiple traveling salesman problem (AHP-MTSP) algorithm is proposed to generate an efficient rescue plan by assigning robots and multiple unmanned aerial vehicles to disaster target locations, while minimizing a set of predefined objectives that depend on the situation. The results demonstrate that immediate feedback obtained from the reconstructed 3D environment can help to investigate whatâif scenarios, allowing for the preparation of effective rescue plans with an appropriate management effort.info:eu-repo/semantics/publishedVersio
A Lightweight Blockchain and Fog-enabled Secure Remote Patient Monitoring System
IoT has enabled the rapid growth of smart remote healthcare applications.
These IoT-based remote healthcare applications deliver fast and preventive
medical services to patients at risk or with chronic diseases. However,
ensuring data security and patient privacy while exchanging sensitive medical
data among medical IoT devices is still a significant concern in remote
healthcare applications. Altered or corrupted medical data may cause wrong
treatment and create grave health issues for patients. Moreover, current remote
medical applications' efficiency and response time need to be addressed and
improved. Considering the need for secure and efficient patient care, this
paper proposes a lightweight Blockchain-based and Fog-enabled remote patient
monitoring system that provides a high level of security and efficient response
time. Simulation results and security analysis show that the proposed
lightweight blockchain architecture fits the resource-constrained IoT devices
well and is secure against attacks. Moreover, the augmentation of Fog computing
improved the responsiveness of the remote patient monitoring system by 40%.Comment: 32 pages, 13 figures, 5 tables, accepted by Elsevier "Internet of
Things; Engineering Cyber Physical Human Systems" journal on January 9, 202
Improved Recursive DV-Hop Localization Algorithm with RSSI Measurement for Wireless Sensor Networks
Abstract: Please refer tp full text to view abstrac
A collaborative demand forecasting process with event-based fuzzy judgements
Mathematical forecasting approaches can lead to reliable demand forecast in some environments by extrapolating regular patterns in time-series. However, unpredictable events that do not appear in historical data can reduce the usefulness of mathematical forecasts for demand planning purposes. Since forecasters have partial knowledge of the context and of future events, grouping and structuring the fragmented implicit knowledge, in order to be easily and fully integrated in final demand forecasts is the objective of this work. This paper presents a judgemental collaborative approach for demand forecasting in which the mathematical forecasts, considered as the basis, are adjusted by the structured and combined knowledge from different forecasters. The approach is based on the identification and classification of four types of particular events. Factors corresponding to these events are evaluated through a fuzzy inference system to ensure the coherence of the results. To validate the approach, two case studies were developed with forecasters from a plastic bag manufacturer and a distributor belonging to the food retailing industry. The results show that by structuring and combining the judgements of different forecasters to identify and assess future events, companies can experience a high improvement in demand forecast accuracy
Regularized Least Square Multi-Hops Localization Algorithm for Wireless Sensor Networks
Abstract: Position awareness is very important for many sensor network applications. However, the use of Global Positioning System receivers to every sensor node is very costly. Therefore, anchor based localization techniques are proposed. The lack of anchors in some Wireless Sensor Networks lead to the appearance of multi-hop localization, which permits to localize nodes even if they are far from anchors. One of the well-known multi-hop localization algorithms is the Distance Vector-Hop algorithm (DV-Hop). Although its simplicity, DV-Hop presents some deficiencies in terms of localization accuracy. Therefore, to deal with this issue, we propose in this paper an improvement of DV-Hop algorithm, called Regularized Least Square DV-Hop Localization Algorithm for multi-hop wireless sensors networks. The proposed solution improves the location accuracy of sensor nodes within their sensing field in both isotropic and anisotropic networks. We used the double Least Square localization method and the statistical filtering optimization strategy, which is the Regularized Least Square method. Simulation results prove that the proposed algorithm outperforms the original DV-Hop algorithm with up to 60%, as well as other related works, in terms of localization accuracy
Privacy Preserving Face Recognition in Cloud Robotics : A Comparative Study
Abstract: Real-time robotic applications encounter the robot on board resourcesâ limitations. The speed of robot face recognition can be improved by incorporating cloud technology. However, the transmission of data to the cloud servers exposes the data to security and privacy attacks. Therefore, encryption algorithms need to be set up. This paper aims to study the security and performance of potential encryption algorithms and their impact on the deep-learning-based face recognition taskâs accuracy. To this end, experiments are conducted for robot face recognition through various deep learning algorithms after encrypting the images of the ORL database using cryptography and image-processing based algorithms
LNT: a logical neighbor tree secure group communication scheme for wireless sensor networks
Secure group communication is a paradigm that primarily designates one-to-many communication security. The
proposed works relevant to secure group communication have predominantly considered the whole network as being
a single group managed by a central powerful node capable of supporting heavy communication, computation and
storage cost. However, a typical Wireless Sensor Network (WSN) may contain several groups, and each one is
maintained by a sensor node (the group controller) with constrained resources. Moreover, the previously proposed
schemes require a multicast routing support to deliver the rekeying messages. Nevertheless, multicast routing can
incur heavy storage and communication overheads in the case of a wireless sensor network. Due to these two major
limitations, we have reckoned it necessary to propose a new secure group communication with a lightweight rekeying
process. Our proposal overcomes the two limitations mentioned above, and can be applied to a homogeneous WSN
with resource-constrained nodes with no need for a multicast routing support. Actually, the analysis and simulation
results have clearly demonstrated that our scheme outperforms the previous well-known solutions
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