Awareness Algorithm Research Articles

Design of Smart Home System based on Wireless Sensor Network Link Status Awareness Algorithm

When wireless sensor networks are used in smart homes, the connection state will be unstable due to signal masking attenuation. This will cause low packet rate, high time delay and high cost in the network. In this paper, a network routing algorithm for wireless sensing based on connection conditions is designed. Secondly, the expected number of sends is proposed to evaluate the stability of links. Based on this, the following network signal delivery situation is forecasted in real time and quickly. According to the estimated expected number of transmissions, the path is dynamically corrected to effectively avoid attenuation in the channel and achieve optimal system performance. Experimental results show that the method proposed in this paper can improve the efficiency of message sending and reduce the routing cost under the condition of masking effect.

Location privacy protection of nodes in the perception layer of the Internet of things based on Radio Frequency Identification location aware algorithm

AbstractTo ensure the security of node location privacy in the perception layer of the Internet of Things, this study proposes a location privacy protection method based on RFID location awareness algorithm. This method first introduces an adaptive multi tree anti‐collision algorithm based on collision trees when perceiving and locating the location information of nodes in the perception layer of the Internet of Things, to prevent signal collisions between multiple readers and writers. Then, based on the obtained node location results, a IoT perception layer node location privacy protection scheme based on virtual ring strategy is used to randomly initiate directed co hop routing using the virtual nodes generated after random walking, avoiding the visible area of the source node location, and transmitting data to the aggregation node in the form of the shortest routing path to protect the privacy and security of the node location. Finally, experiments are carried out to prove the progressiveness of the proposed method. The experimental results show that this method can prolong the security period of the source and aggregation nodes in the perception layer of the Internet of Things, protect the privacy and security of node positions, and has great application value.

Design of a new kind of chemical experiment container with virtual reality fusion

BackgroundAt present, the teaching of experiments in primary and secondary schools is affected by cost and security factors. Existing research on virtual experiment platforms has alleviated these problems. However, the lack of real experimental equipment and use of a single channel to understand user intentions weaken these platforms operationally and degrade the naturalness of interactions. MethodsTo solve these problems, we propose an intelligent experimental container structure and a situational awareness algorithm, both of which are verified and applied to a chemical experiment involving virtual-real fusion. First, the acquired images are denoised in the visual channel using the maximum diffuse reflection chroma to remove overexposure. Second, container situational awareness is realized by segmenting the image liquid level and establishing a relation-fitting model. Then, strategies for constructing complete behaviors and making priority comparisons among behaviors are adopted for information complementarity and independence, respectively. A multichannel intentional understanding model and an interactive paradigm that integrates vision, hearing, and touch are proposed. ResultsThe experimental results show that the accuracy of the intelligent container situation awareness proposed in this paper reaches 99%, and the accuracy of the proposed intention understanding algorithm reaches 94.7%. The test shows that the intelligent experimental system based on the new interaction paradigm also has better performance and a more realistic sense of operation experience in terms of experimental efficiency. ConclusionThe results indicate that the proposed experimental container and algorithm can achieve a natural level of human-computer interaction in a virtual chemical experiment platform, enhance the user′s sense of operation, and achieve high levels of user satisfaction.

Leveraging cross-view geo-localization with ensemble learning and temporal awareness.

The Global Navigation Satellite System (GNSS) is unreliable in some situations. To mend the poor GNSS signal, an autonomous vehicle can self-localize by matching a ground image against a database of geotagged aerial images. However, this approach has challenges because of the dramatic differences in the viewpoint between aerial and ground views, harsh weather and lighting conditions, and the lack of orientation information in training and deployment environments. In this paper, it is shown that previous models in this area are complementary, not competitive, and that each model solves a different aspect of the problem. There was a need for a holistic approach. An ensemble model is proposed to aggregate the predictions of multiple independently trained state-of-the-art models. Previous state-of-the-art (SOTA) temporal-aware models used heavy-weight network to fuse the temporal information into the query process. The effect of making the query process temporal-aware is explored and exploited by an efficient meta block: naive history. But none of the existing benchmark datasets was suitable for extensive temporal awareness experiments, a new derivative dataset based on the BDD100K dataset is generated. The proposed ensemble model achieves a recall accuracy R@1 (Recall@1: the top most prediction) of 97.74% on the CVUSA dataset and 91.43% on the CVACT dataset (surpassing the current SOTA). The temporal awareness algorithm converges to R@1 of 100% by looking at a few steps back in the trip history.

Research on elaborate image simulation method for close-range space target

In order to meet the urgent needs of target image data in the testing of close-range space target situational awareness algorithm, an elaborate image simulation method that fully considers the target surface texture structure is proposed. First, the physical mechanism is analysed by constructing a radiation transmission model. Then, Phong and five-parameters BRDF models are adopted to characterize the optical properties of the material. The geometric characteristic of texture structure is also characterized through displacement mapping. Finally, the sequence images of the space target are obtained by simulation based on the designed simulation process and given imaging conditions. The CPU multi-core parallel acceleration based on the Embree engine is taken to compensate for the speed reduction caused by the increase in the number of model faces. Compared with the measured image in orbit for verification, the image simulation similarity is above 80%. The results show that the simulation method in this article can better describe the characteristics of the target in the actual on-orbit environment and meet the near real-time simulation requirements of the system, providing a more realistic image data source for the ground test of the on-orbit service technology.

Research on Underwater Wireless Acoustic Communication Routing Protocol

Due to the limitation of the communication range of sensor nodes, underwater nodes must transmit data to the water surface through routing mechanism. Therefore, the design of routing algorithm is related to the performance of the network, and the research of routing algorithm is necessary. However, underwater nodes are vulnerable to ocean turbulence and biological factors. The underwater environment is dynamic and complex, which greatly affects the acoustic channel, making the improvement of routing protocol a challenge. These factors lead to problems in underwater acoustic communication network, such as empty nodes, packet delivery rate, energy consumption, delay and other problems, and in the dynamic and complex seawater environment, the performance of acoustic network will be further reduced. This paper introduces some main routing algorithms, such as hollow node awareness algorithm, layer-based routing protocol; Opportunistic routing algorithm; Double-hop routing protocol, etc. These routing algorithms can solve some problems. Simulation shows that they have certain advantages in packet delivery rate, delay, network lifetime, etc.

Flapless dental implant placement using a recently developed haptic robotic system

A recent technological breakthrough in implant surgery is the use of robotic guidance (Yomi®, Neocis Inc).1–3 The technique aims to mitigate human-based error and eliminate the use of static surgical guides by algorithmically directing surgeons according to their virtual treatment plan.2 The system’s spatial awareness algorithm fixes the surgeon’s handpiece prior to initiating surgery to ensure that the surgeon is at the correct angulation and depth, and can maintain an adequate safe distance from key anatomical structures.

Development of a wide-range soft sensor for predicting wastewater BOD5 using an eXtreme gradient boosting (XGBoost) machine

In wastewater monitoring, detecting extremely high pollutant concentrations is necessary to properly calibrate the treatment process. However, existing hardware sensors have a limited linear range which may fail to measure extremely high levels of pollutants; and likewise, the conventional “soft” model sensors are not suitable for the highly-skewed data distributions either. This study developed a new soft sensor by using eXtreme Gradient Boosting (XGBoost) machine learning to ‘measure’ the wastewater organics (in terms of 5-day biochemical oxygen demand (BOD5)). The soft sensor was tested on influent and effluent BOD5 of two different wastewater treatment plants to validate the results. The model results showed that XGBoost can detect these extreme values better than conventional soft sensors. This new soft sensor can function using a sparse input matrix via XGBoost's sparsity awareness algorithm - which can address the limitation of the conventional soft sensor with the fallibility of supporting hardware sensors even.

K-means+SSA-Elman network visible light indoor location awareness algorithm

K-means+SSA-Elman network visible light indoor location awareness algorithm

SDAE+Bi-LSTM-Based Situation Awareness Algorithm for the CAN Bus of Intelligent Connected Vehicles

With a deep connection to the internet, the controller area network (CAN) bus of intelligent connected vehicles (ICVs) has suffered many network attacks. A deep situation awareness method is urgently needed to judge whether network attacks will occur in the future. However, traditional shallow methods cannot extract deep features from CAN data with noise to accurately detect attacks. To solve these problems, we developed a SDAE+Bi-LSTM based situation awareness algorithm for the CAN bus of ICVs, simply called SDBL. Firstly, the stacked denoising auto-encoder (SDAE) model was used to compress the CAN data with noise and extract the deep spatial features at a certain time, to reduce the impact of noise. Secondly, a bidirectional long short-term memory (Bi-LSTM) model was further built to capture the periodic features from two directions to enhance the accuracy of the future situation prediction. Finally, a threat assessment model was constructed to evaluate the risk level of the CAN bus. Extensive experiments also verified the improved performance of our SDBL algorithm.

RETRACTED ARTICLE: Quantitative situational awareness algorithm of land state network based on neutral statistics

In order to improve the low accuracy of the traditional algorithm, a quantitative land state network situation awareness algorithm based on neutral statistics is proposed. According to the definition of neutral statistical awareness algorithm, the quantitative awareness model of land state cyberspace situation is constructed from the perspectives of attack event and security vulnerability awareness, security situation modeling and associated threat assessment. Determine the malicious nodes, analyze the state function, and design a quantitative perception process. From the simulation results, we can see that the method proposed in this paper can fully adapt to various information processing work, and has the advantages of low computational complexity, short evaluation time, high evaluation accuracy, and the algorithm situation quantitative is basically consistent with the actual value, the error is also less than 0.1, which has good practical application effect.

An efficient scheduling algorithm for dataflow architecture using loop-pipelining

Dataflow architecture has native advantages in achieving high instruction parallelism and power efficiency for today’s emerging applications such as high performance computing and deep neural network. In dataflow computing, the execution of instructions is driven by data, so the data transfer efficiency of the network on chip (NoC) is a key factor affecting performance. However, the NoC performance degrades due to the increasing use of multicast communications in many applications. The existing dataflow architecture instruction scheduling algorithms do not optimize multicast communication between the instruction and its successor instructions, so the routing paths of many multicast packets have forks which cause bandwidth waste and potential network congestion. We propose a sharing path awareness (SPA) algorithm to optimize multicast communication in the dataflow architecture. The algorithm shares the routing paths from the instruction to its child node to reduce the NoC bandwidth waste through the instruction scheduler. For applications using software iteration, we further extend the loop optimization to the SPA algorithm to sufficiently exploit instruction-level parallelism. Compared with the state-of-the-art algorithm, we show that the SPA algorithm achieves 20.21% average performance improvement and 15.11% energy consumption reduction for our experimental workloads.

Naive echo-state-network based services awareness algorithm of software defined optical networks

As internet services newly emerge with diversity and complexity, great challenges and demands are presented to the OpenFlow controlled software defined optical networks (SDON) to achieve better match between services and SDON. With this aim, this paper proposes a naive Echo-State-Network (Naive-ESN) based services awareness algorithm of the software defined optical network, where the naive ESN model adopts the ring topology structure and generates the probability output result to determine the QoS policy of SDON. Moreover, the Naive-ESN engine is also designed in controller node of SDON to perform services awareness by obtaining service traffic features from data plan, together with some necessary extension of the OpenFlow protocol. Test results show that the proposed approach is able to improved services-oriented supporting ability of SDON.

Firefly Algorithm for Intelligent Context-Aware Sensor Deployment Problem in Wireless Sensor Network

Wireless sensor networks (WSNs) provide acceptable low cost and efficient deployable solutions to execute the target tracking, checking and identification of substantial measures. The primary step necessary for WSN is to organize all the sensor nodes in their positions to build up an effective network. In the sensor deployment model, Target COVerage (TCOV) and Network CONnectivity (NCON) are the basic issues in WSNs that have obtained significant consideration in sensor deployment. This paper intends to develop an intelligent context awareness algorithm for sensor deployment process in WSN. Accordingly, the process is divided into two phases. In the first phase, the TCOV process is performed, whereas the second phase of the algorithm establishes NCON among the sensors. An objective model to meet both TCOV and NCON is formulated as a minimization problem. The problem is solved using FireFly (FF) optimization to determine the optimal locations for sensors. It leads to an intelligent sensor deployment model that can determine the optimal locations for the sensors in the WSN. Further, the proposed FF-TCOV and FF-NCON models are compared against the conventional algorithms, namely, genetic algorithm, particle swarm optimization, artificial bee colony, differential evolution and evolutionary algorithm-based TCOV and NCON models. The results achieved from the simulation show the improved performance of the proposed technique.

Energy Consumption Estimation Framework at Source Level for Data Centers

In a cloud computing environment, you can use source-level energy consumption (EC) estimation to estimate the amount of energy consumed by the cloud before the cloud computing tasks are performed.EC estimation of tasks is important for source code improvement in the aspect of task scheduling and EC optimization. We propose a cloud data centre. Here to perform a task on our cloud data centre and calculate the energy. We use energy awareness algorithm to calculate the energy of the task. Finally, we compare the energy required to perform the task on local machine and cloud. We show that our proposed system is more efficient than the existing system.

Research on Situation Awareness of Airport Operation Based on Petri Nets

The civil aviation industry is undergoing rapid development. However, the on-time rate of airport flights and passenger service quality are not particularly satisfying. The cause of the above problems is the contradiction between the limited operational support capability and the continuous growth of passenger traffic volume. Therefore, the key to solving these problems is achieving situation awareness of airport operation. Many situation awareness algorithms, typically categorized into modeling and machine learning, have been proposed in the past years. However, existing models lack flexibility and their prediction accuracy is unstable. Machine learning’s results cannot be timely and effective when external conditions are suddenly changed although some related algorithms have higher accuracy because of the retraining of artificial neural network (ANN). This paper proposes a situation awareness method based on Petri nets (PNs). This method introduces the queuing theory and perceptual parameters into the existing PN and constructs the perceptual PNs’ model for general service systems so that it can quickly model different scene service systems. In combination with the ANN, this paper proposes a complete situation awareness algorithm to realize a sustained and accurate situation awareness prediction of the service system by solving point estimations of the macroscopic and microscopic situation in this model, which helps to address some challenges faced by current civil aviation airports. By experimenting on-ground support in civil aviation airports and the access of website as well as comparing the situation separately with Airport Collaborative Decision Making and stochastic PNs, the validity and accuracy of the algorithm proposed in this paper are well verified.

Deployment of Context-Aware Sensor in Wireless Sensor Network Based on the Variants of Genetic Algorithm

Wireless sensor networks (WSNs) are generally a group of spatially scattered and devoted sensors to record and monitor the physical environmental condition, and the collected data is grouped at a central location. In fact, the environmental conditions such as sound, humidity, temperature, wind, pollution levels, etc., can be clearly determined by WSNs. The principal objective of WSNs is to organize the whole sensor nodes in their related positions, thereby developing an effective network. In WSNs, target COVerage (TCOV) and Network CONnectivity (NCON) are the main concern of the sensor deployment problem. Many research works aspire the evolvement of smart context awareness algorithm for sensor deployment issues in WSN. Here the TCOV and NCON process are deployed as the minimization problem. This article makes an analysis of different GA variations in attaining the objective. The GA variations are as follows: self-adaptive genetic algorithm (SAGA), deterministic-adaptive genetic algorithm (DAGA), Individual- Adaptive Genetic Algorithm (IAGA). Finally, the methods are compared to one another in terms of connectivity and coverage performance.

Review on IMS-4G-cloud networks maintaining service continuity using distributed multi agent scheme

In the current networking field, research works are going on with the aim of providing user%s desired QoS everywhere. This paper discusses the distributed multi agent scheme (DMAS) scheme used in the IMS-4G-cloud networks to support session (service) continuity. It explains the role of different agents which make use of knowledge sources in solving the problem and performing tasks. A Q-learning awareness algorithm is used to estimate the QoS by calculating cost function with the help of mathematical formulae comprising of QoS parameters like jitter, delay, packet loss, network resource availability, mobility and service fare parameter. Agents in the DMAS interact with each other and work cooperatively to provide both QoS and mobility information. Based on the type of information provided, two types of handoff came in to existence. A detailed explanation of these handoffs with phases is discussed in this paper. Finally, a comparison is made between general IMS handoff procedure and DMAS-IMS handoff procedure with the help of signal flow diagram.

Implementation of Emergency Monitoring System using Biometric Data

Most emergencies faced by the socially vulnerable class occur in the absence of their protectors or observers. Therefore, unexpected accidents lead to taking their lives. To solve the problem, it is required to establish a system enable to notify a protector or an observer of an emergency in order to prevent and deal with an unexpected accident. An emergency monitoring system using biometric data receives biosignals from wearable devices, detects and judges an emergency with the use of emergency context awareness algorithm, and then gives a notification to a protector or an observer. The monitoring system implemented in this study is able to collect body temperature, heart rate, and work rate to recognize an emergency and to make a judgment on the basis of the received biosignals with the use of emergency context awareness algorithm.

Cooperative Autonomous Driving: A Mirror Neuron Inspired Intention Awareness and Cooperative Perception Approach

In this article, we present the concept of cooperative autonomous driving using mirror neuron inspired intention awareness and cooperative perception, whose primary benefit is to enable cooperative autonomous driving in a more general approach rather than complicated rule-based approaches. The cooperative perception can provide information on the upcoming traffic situations ahead, even beyond line-of-sight and field-of-view. From a control perspective, a spatial map for navigation planning is extended up to the boundary of connected vehicles in a see-through manner. By leveraging this augmented perception capability, a better driving decision can be accomplished in terms of traffic flow efficiency and safety improvement. For this purpose, we propose a mirror neuron inspired intention awareness algorithm along with planning and control methods for cooperative autonomous driving. We demonstrate the feasibility of our proposals through simulations and experiments on the road with a cooperative lane changing scenario.