Sensor Resources Research Articles

Sensory and decisional components of endogenous attention are dissociable.

Endogenous cueing of attention enhances sensory processing of the attended stimulus (perceptual sensitivity) and prioritizes information from the attended location for guiding behavioral decisions (spatial choice bias). Here, we test whether sensitivity and bias effects of endogenous spatial attention are under the control of common or distinct mechanisms. Human observers performed a multialternative visuospatial attention task with probabilistic spatial cues. Observers’ behavioral choices were analyzed with a recently developed multidimensional signal detection model (the m-ADC model). The model effectively decoupled the effects of spatial cueing on sensitivity from those on spatial bias and revealed striking dissociations between them. Sensitivity was highest at the cued location and not significantly different among uncued locations, suggesting a spotlight-like allocation of sensory resources at the cued location. On the other hand, bias varied systematically with cue validity, suggesting a graded allocation of decisional priority across locations. Cueing-induced modulations of sensitivity and bias were uncorrelated within and across subjects. Bias, but not sensitivity, correlated with key metrics of prioritized decision-making, including reaction times and decision optimality indices. In addition, we developed a novel metric, differential risk curvature, for distinguishing bias effects of attention from those of signal expectation. Differential risk curvature correlated selectively with m-ADC model estimates of bias but not with estimates of sensitivity. Our results reveal dissociable effects of endogenous attention on perceptual sensitivity and choice bias in a multialternative choice task and motivate the search for the distinct neural correlates of each.NEW & NOTEWORTHY Attention is often studied as a unitary phenomenon. Yet, attention can both enhance the perception of important stimuli (sensitivity) and prioritize such stimuli for decision-making (bias). Employing a multialternative spatial attention task with probabilistic cueing, we show that attention affects sensitivity and bias through dissociable mechanisms. Specifically, the effects on sensitivity alone match the notion of an attentional “spotlight.” Our behavioral model enables quantifying component processes of attention, and identifying their respective neural correlates.

Distributed two‐stage state estimation with event‐triggered strategy for multirate sensor networks

SummaryThis paper investigates the state estimation issue for a class of wireless sensor networks (WSNs) with the consideration of limited energy resources. First, a multirate estimation model is established, and then, a new event‐triggered two‐stage information fusion algorithm is developed based on the optimal fusion criterion weighted by matrices. Compared with the existing methods, the presented fusion algorithm can significantly reduce the communication cost in WSNs and save energy resources of sensors efficiently. Furthermore, by presetting a desired containment probability over the interval [0,1] with the developed event‐triggered mechanism, one can obtain a suitable compromise between the communication cost and the estimation accuracy. Finally, a numerical simulation for the WSN tracking system is given to demonstrate the effectiveness of the proposed method.

Hierarchy Graph Based Barrier Coverage Strategy with a Minimum Number of Sensors for Underwater Sensor Networks.

Underwater sensor networks based barrier coverage is increasingly important for intrusion detection due to the scarcity of underwater sensor resource. To improve UWSNs’ detection performance and prolong their lifetime, an efficient barrier coverage strategy is very important. In this paper, a novel concept: hierarchy graph is proposed. Hierarchy graph can make the network’s topology more clarity. In accordance with the hierarchy graph, 1-barrier coverage algorithm and k-barrier coverage algorithm are presented to construct the barrier with less sensors for higher energy efficiency. Both analytical and simulation studies demonstrate that the proposed algorithms can provide high detection probability and long lifetime for UWSNs.

AQ-Routing: mobility-, stability-aware adaptive routing protocol for data routing in MANET–IoT systems

Internet of Things, is an innovative technology which allows the connection of physical things with the digital world through the use of heterogeneous networks and communication technologies. In an IoT system, a major role is played by the wireless sensor network as its components comprise: sensing, data acquiring, heterogeneous connectivity and data processing. Mobile ad-hoc networks are highly self reconfiguring networks of mobile nodes which communicate through wireless links. In such a network, each node acts both as a router and host at the same time. The interaction between MANETs and Internet of Things opens new ways for service provision in smart environments and challenging issues in its networking aspects. One of the main issues in MANET–IoT systems is the mobility of the network nodes: routing protocol must react effectively to the topological changes into the algorithm design. We describe the design and implementation of AQ-Routing, and analyze its performance using both simulations and measurements based on our implementation. In general, the networking of such a system is very challenging regarding routing aspects. Also, it is related to system mobility and limited network sensor resources. This article builds upon this observation an adaptive routing protocol (AQ-Routing) based on Reinforcement Learning (RL) techniques, which has the ability to detect the level of mobility at different points of time so that each individual node can update routing metric accordingly. The proposed protocol introduces: (i) new model, developed via Q-learning technique, to detect the level of mobility at each node in the network; (ii) a new metric, called $$Q_{\textit{metric}},$$ which account for the static and dynamic routing metrics, and which are combined and updated to the changing network topologies. The protocol can efficiently handle network mobility by a way of preemptively adapting its behaviour thanks to the mobility detection model. The presented results of simulation provide an effective approach to improve the stability of links in both static and mobile scenario and, hence, increase the packet delivery ratio in the global MANET–IoT system.

Personalized Mobility Interventions Using Smart Sensor Resources for Lower-Limb Prosthesis Users

Personalized Mobility Interventions Using Smart Sensor Resources for Lower-Limb Prosthesis Users

A Feature-Based Learning System for Internet of Things Applications

In many applications of Internet of Things (IoT), the huge amount of data are generated by sensor nodes and processing them are complex. Offloading data classification and anomaly event detection tasks to sink nodes in sensor networks can reduce the computing complexity, lower remote communication loads, and improve the response time for the delay-sensitive IoT applications. Many existing classification and anomaly detection methods cannot be directly applied to these IoT applications, because the computing and energy resources of sensors are limited. In this paper, a new feature-based learning system for IoT applications is proposed to effectively classify data and detect anomaly event. Especially, based on the theory of distributed compression, the sparsity and relativity of the data are exploited to obtain the classification features, which can reduce the computation overhead and energy consumption. Further, an RBF-BP hybrid neural network is employed to detect the anomaly event based on the classification results, by which the training time of neural network can be significantly reduced and the accuracy can be improved for users’ decisions.

Research on Multi-Sensor Resource Dynamic Allocation Auction Algorithm

This paper designs a multi-sensor resource dynamic allocation method based on auction algorithm. Tasks are prioritized according to the needs of the engineering field. Task priority is used as the basis for multi-sensor resource allocation order, taking into account the target's threat value and information needs. The sensor and task pairing function is established and used to measure the sensor resource dynamic allocation, we also use Analytic Hierarchy Process to determine the weight of each performance parameter in the pairing function (such as detection probability, intercept probability, positioning accuracy, tracking accuracy, recognition probability, etc.). The auction algorithm is improved by adding resource dynamic allocation constraints, which not only ensures the continuous execution of the target task, but also improves the dynamic allocation efficiency of multi-sensor resources. The simulation results show that the allocation method in this paper is scientific and reasonable.

Olfactory Navigation and the Receptor Nonlinearity.

The demands on a sensory system depend not only on the statistics of its inputs but also on the task. In olfactory navigation, for example, the task is to find the plume source; allocation of sensory resources may therefore be driven by aspects of the plume that are informative about source location, rather than concentration per se. Here we explore the implications of this idea for encoding odor concentration. To formalize the notion that sensory resources are limited, we considered coding strategies that partitioned the odor concentration range into a set of discriminable intervals. We developed a dynamic programming algorithm that, given the distribution of odor concentrations at several locations, determines the partitioning that conveys the most information about location. We applied this analysis to planar laser-induced fluorescence measurements of spatiotemporal odor fields with realistic advection speeds (5-20 cm/s), with or without a nearby boundary or obstacle. Across all environments, the optimal coding strategy allocated more resources (i.e., more and finer discriminable intervals) to the upper end of the concentration range than would be expected from histogram equalization, the optimal strategy if the goal were to reconstruct the plume, rather than to navigate. Finally, we show that ligand binding, as captured by the Hill equation, transforms odorant concentration into response levels in a way that approximates information maximization for navigation. This behavior occurs when the Hill dissociation constant is near the mean odor concentration, an adaptive set-point that has been observed in the olfactory system of flies.SIGNIFICANCE STATEMENT The first step of olfactory processing is receptor binding, and the resulting relationship between odorant concentration and the bound receptor fraction is a saturating one. While this Hill nonlinearity can be viewed as a distortion that is imposed by the biophysics of receptor binding, here we show that it also plays an important information-processing role in olfactory navigation. Specifically, by combining a novel dynamic-programming algorithm with physical measurements of turbulent plumes, we determine the optimal strategy for encoding odor concentration when the goal is to determine location. This strategy is distinct from histogram equalization, the strategy that maximizes information about plume concentration, and is closely approximated by the Hill nonlinearity when the binding constant is near the ambient mean.

Du retrait à la reconquête : pratiquer la ville après un épisode psychotique

Abstract. The article aims to provide a better understanding of the urban practices of young people living with a diagnosis of psychosis while recovering. I show the way practices are adjusted according to the temporal dynamics of psychosis. I argue that the continuous variability of symptoms over the recovery period implies alternately practices of withdrawal and reconquest of the urban space. I first outline participants' reconquest of urban spaces, which starts in well-known places and then extends to less familiar ones. In doing so, I point out the diversity of urban spaces inhabited by participants during the recovery process which includes institutional, private, as well as public places. I then outline the various material, relational and sensory resources available in these spaces. I show how participants use them according to the temporal dynamics. I finally highlight the way participants are gradually getting involved in the relationship with a large array of resources as the intensity of symptoms is reducing. My analysis is based on a three months ethnography in a therapeutic institution in Lausanne.

RELOAD/CoAP architecture for the federation of wireless sensor networks

Sensing devices are expected to interconnect over large geographical areas and federations of wireless sensor networks are expected in a near future. In such environments a critical issue is how to discover the resources available at devices in a scalable manner. For this purpose, a Constrained Application Protocol (CoAP) Usage for REsource LOcation And Discovery (RELOAD), a generic self-organizing Peer-to-Peer (P2P) overlay network service, has been defined to be used as a lookup service, to store available device resources and as a cache for sensor data. Each P2P resource, at the RELOAD/CoAP overlay, includes references to device resources, hosted at one or more constrained nodes, but no provision is made for the insertion of bindings/references to P2P resources already available at the P2P overlay network. Such feature would increase the efficiency and consistency of storage, avoiding duplicate references, a very relevant issue for future IoT applications relying on the federation of wireless sensor networks. In this article an extension to the service provided by CoAP Usage is proposed so that resource bindings can be managed. Two binding models, and a heuristic algorithm for their implementation, are proposed. Results show that such models lead to a better resource organization, reducing the number of sensor resource entries and/or fetches to the P2P overlay.

Fault Prediction Method for Wireless Sensor Network Based on Evidential Reasoning and Belief-Rule-Base

Wireless sensor network (WSN) is a distributed intelligent network, which can independently achieve the information collection task of monitoring targets. However, the WSN is susceptible to faults due to various factors, such as sensor resources, network bandwidth, and work environment. The WSN fault prediction technology can estimate the fault trend of the WSN, which can provide the basis for the formulation and implementation of emergency strategies. In this paper, a new WSN fault prediction method is proposed based on evidential reasoning (ER) and belief rule base (BRB). First, the process of the WSN fault prediction is described, which mainly includes the fault assessment of the current WSN and the fault prediction of future WSN. Second, the WSN fault prediction model is constructed, including the ER-based fault assessment model and BRB-based fault prediction model. The projection covariance matrix adaptation evolutionary strategies (P-CMA-ESs) are used to optimize model parameters. Finally, a case study is constructed to verify the validity of the WSN fault prediction model. The experimental results show that the model can adequately estimate the fault state of the current WSN and then predict the fault status of future WSN.

An Efficient Preference-Based Sensor Selection Method in Internet of Things

While it is well understood that the Internet of things (IoT) can facilitate numerous applications (e.g., environmental supervision, forest fire prevention and Intelligent farming), it also brings a significant challenge for efficiently selecting sensors that meet users' preference and specific requirement from millions of heterogeneous sensors. In this paper, we propose an improved fast nondominated sorting algorithm for efficiently preference-based sensor selection in IoT. Specifically, this proposed method mainly includes three parts: 1) Offline constructing R-tree to search sensor resources and narrowing the size of dataset according to user's preference; 2) Using an improved fast nondominated sorting approach to get nondominated front; 3) Employing TOPSIS to characterize every sensor option of the nondominated front. In order to illustrate the usability of the model, we conduct experiments on several simulation datasets. Experimental results show that this method outperforms several baselines in terms of both response time and accuracy.

Evaluation of a Visual–Tactile Multimodal Display for Surface Obstacle Avoidance During Walking

The future of human space exploration will involve extra-vehicular activities (EVAs) on foreign planetary surfaces (i.e., Mars), an activity that will have significantly different characteristics than exploration scenarios on Earth. These activities become challenging due to restricted vision and limitations placed on sensory feedback from altered gravity and the space suit. The use of a bulky, pressurized EVA suit perceptually disconnects human explorers from the hostile environment, increasing navigation workload and the risk of collision associated with traversing through unfamiliar terrain. Multimodal displays are considered as a means to provide information to the astronaut as cues to multiple sensory modalities enhance cognitive processing through taking advantage of multiple sensory resources, and are believed to communicate the risk more efficiently than unimodal cues. This paper presents a wearable multimodal interface system to examine human performance when visual, vibratory, and visual–vibratory cues are provided to aid in ground obstacle avoidance. The wearable system applies vibrotactile cues to the feet and visual cues through augmented reality glasses to convey obstacle location and proximity. Participants ( n = 16) performed an overground obstacle avoidance task with the multimodal device. Performance metrics included completion time, subjective workload, head-down time, collisions, as well as gait parameters. Results indicate that information displays enhance task performance, with the visual-only display promoting the least head-down time over tactile-only or visual–tactile displays. Head-down time was the highest for trials without a display. Results provide implications for presenting information during physically active tasks such as suited obstacle avoidance.

‘We're off to the market’

Sal McKeown discovers how interactive, sensory resources from Positive Eye are supporting young children with a visual impairment to explore the world and enjoy learning along with their peers.

Sensor management of LEO constellation using modified binary particle swarm optimization

In this paper, we are concerned with the problem of utilizing a low Earth orbit (LEO) infrared constellation in order to track the midcourse ballistic missile. Due to the limited sensor resources of LEO constellation, an appropriate sensor management strategy is necessary. The posterior Cramér-Rao lower bound (PCRLB) is used as the basis for sensor management of LEO constellation. Moreover, a modified binary particle swarm optimization (MBPSO) algorithm is presented for use in finding the optimal allocation scheme of sensors for maximizing the tracking accuracy. The proposed MBPSO algorithm adopts a different transfer function and a new method to generate initial population. The MBPSO’s performance and the PCRLB-based sensor management strategy are verified by applying to a case study.

Sensory-based intervention in the schools: a survey of occupational therapy practitioners

ABSTRACTAim: The primary purpose of this study was to investigate the role of sensory-based intervention in school-based occupational therapy. The research questions are: 1. How do school-based occupational therapists (OTs) describe the role of sensory-based intervention (SBI) in an educational setting? 2. Which current practice trends influence sensory-based occupational therapy practice?Methods: Descriptive statistics were generated to summarize current school-based practice trends. Qualitative analysis resulted in the identification of categories related to school-based practice. Participants (N = 94) reported the positive impact of sensory-based interventions in the schools, the challenge of the school context, the value of both teacher and team collaboration, the limitations and challenges to parent collaboration, and the sensory resources within the school context.Results: Results indicate that school-based practitioners should focus on educational and social participation of the students.Conclusion: Outcomes indicate the primary means of improving student participation is via team education and advocacy.

A target group tracking algorithm based on a hybrid sensor network**Project supported by the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20140875), the Scientific Research Foundation of Nanjing University of Posts and Telecommunications, China (Grant No. NY213084), and the National Natural Science Foundation of China (Grant No. 61502243).

Traditional tracking algorithms based on static sensors have several problems. First, the targets only occur in a part of the interested area; however, a large number of static sensors are distributed in the area to guarantee entire coverage, which leads to wastage of sensor resources. Second, many static sensors have to remain in active mode to track the targets, which causes an increase of energy consumption. To solve these problems, a target group tracking algorithm based on a hybrid sensor network is proposed in this paper, which includes static sensors and mobile sensors. First, an estimation algorithm is proposed to estimate the objective region by static sensors, which work in low-power sensing mode. Second, a movement algorithm based on sliding windows is proposed for mobile sensors to obtain the destinations. Simulation results show that this algorithm can reduce the number of mobile sensors participating in the tracking task and prolong the network lifetime.

Depletion-of-Battery Attack: Specificity, Modelling and Analysis.

The emerging Internet of Things (IoT) has great potential; however, the societal costs of the IoT can outweigh its benefits. To unlock IoT potential, there needs to be improvement in the security of IoT applications. There are several standardization initiatives for sensor networks, which eventually converge with the Internet of Things. As sensor-based applications are deployed, security emerges as an essential requirement. One of the critical issues of wireless sensor technology is limited sensor resources, including sensor batteries. This creates a vulnerability to battery-exhausting attacks. Rapid exhaustion of sensor battery power is not only explained by intrusions, but can also be due to random failure of embedded sensor protocols. Thus, most wireless sensor applications, without tools to defend against rash battery exhausting, would be unable to function during prescribed times. In this paper, we consider a special type of threat, in which the harm is malicious depletion of sensor battery power. In contrast to the traditional denial-of-service attack, quality of service under the considered attack is not necessarily degraded. Moreover, the quality of service can increase up to the moment of the sensor set crashes. We argue that this is a distinguishing type of attack. Hence, the application of a traditional defense mechanism against this threat is not always possible. Therefore, effective methods should be developed to counter the threat. We first discuss the feasibility of rash depletion of battery power. Next, we propose a model for evaluation of energy consumption when under attack. Finally, a technique to counter the attack is discussed.

Development and experimental validation of a multi-algorithmic hybrid attitude determination and control system for a small satellite

Advanced missions of satellites are increasingly demanding more accurate and robust attitude maneuvering capabilities. However, it is difficult to achieve especially for small satellites due to limited hardware resources of sensors, actuators, and processors. In this paper, to achieve the desired performance, a multi-algorithmic hybrid attitude determination and control system (ADCS) that utilizes a family of control and estimation algorithms is developed and implemented in numerical simulations and experiments for a small satellite. The hybrid automaton framework of the ADCS is designed to accomplish the desired performance with the limited hardware capability by switching the control and estimation algorithms effectively for given situations in space. The performance of the hybrid ADCS is evaluated through numerical and hardware-in-the-loop simulations that are based on a three-dimensional air-bearing testbed, CubeSat Three-Axis Simulator (CubeTAS). Simulation and experimental results demonstrate the effectiveness of the multi-algorithmic hybrid ADCS. The significance of this paper is in demonstrating that the hybrid automaton framework can be an effective approach to handle operational situations in space. It also provides a design reference for a small satellite ADCS.

Sensor Tasking for Spacecraft Custody Maintenance and Anomaly Detection Using Evidential Reasoning

Current state-of-the-art sensor tasking for space situational awareness is human-analyst intensive, reactive, and individualized for specific sensors. In application, there are many conflicting objectives and hypotheses that compete for sensor resources, which complicates the sensor assignment problem. Using the Dempster–Shafer theory of evidence, this work proposes a sensor-tasking criterion based on minimizing ignorance in hypothesis resolution. An approach for formulating space situational awareness sensors as evidence-based experts is presented, and an algorithmic implementation for the joint space-object custody and anomaly detection problem is developed and tested. Results show an automated, predictive capability to schedule a sensor network that successfully maintains custody in the presence of poor observation conditions and spacecraft maneuvers. In comparison to covariance minimization, the ignorance-reduction technique resolves hypotheses as well or better while using fewer actions.