Sensor Resources Research Articles

English

The ubiquitous nature of wireless sensor networks (WSNs) means that their underlying network architecture may be used to serve a broad range of use cases, from "smart cities" to "smart homes" and beyond. Other serious problems, such as energy use and security, have persisted. When it comes to WSN security, the blockchain might be the answer. Traditional blockchains have limitations, such as inefficient transaction processing and limited scalability. When Wireless Sensor Networks (WSNs) are deployed, most of the time, the nodes are left unmanaged and vulnerable to various security threats. Reliable data distribution in WSNs is nontrivial because of the limited resources and dynamic behavior of sensor nodes. Traditional cryptography and authentication-based methods have been shown to be unsuitable for countering node misbehavior attacks due to their high cost and incapability. The lightweight authentication encryption system was developed to solve the problems of sensor resource scarcity and data security (data integrity and secrecy). Current trust-based solutions, on the other hand, increase traffic congestion and decrease the network's lifespan due to the high costs associated with trust estimates and network-wide dissemination. As a result, the objective of this study is to present an intrusion prevention architecture for mobile Internet of Things devices, along with its integration into WSN, to ensure data security while simultaneously improving the network delivery ratio. The architecture that has been suggested is made up of two different discrete components. To begin, the ambiguity principle is used to construct non-overlapping and independently structured clusters, which are then used to preserve the clusters' stability. Second, based on the architecture of the blockchain, encrypted multi-hop routing pathways with end-to-end coverage are being established. By reducing end-to-end latency, time consumption, packet loss rate, and energy usage while increasing throughput, our recommended model's simulation results reveal good results and security gains.

An Online Resource Management for Obscured Sensors in Agriculture using UAV

Agriculture-based Internet of Things (Ag-IoT) can generate near-real-time quantitative data to analyze crop growth. Unmanned Aerial Vehicle (UAV) based mobile edge computing systems is widely used in Ag-IoT due to the low cost, fast movement and ease of operation of UAVs. Nevertheless, sensors in agriculture may be under a tall and dense crop canopy or under the soil where they have difficulty harvesting a steady of energy and have too worse a channel condition to transmit more data. This paper considers a UAV-Sensor Collaboration Wireless Network (USCWN) in which each UAV carries a computation module and a wireless power transfer module to provide service and energy to sensors. Then, we propose an online resource management framework with two sub-algorithms to maximize the network throughput of USCWN. The first is the sensor fuzzy selection algorithm, which determines which UAV the sensor transmits data to, considering the different resource properties between sensors and UAVs without prior knowledge. The other is a semi-distributed dynamic resource allocation algorithm in which the sensors compete with UAV’s energy, computation, and communication resources in a stochastic game. The UAV acts as a game manager to manage sensor resources. The stochastic game is solved using Lyapunov optimization, which yields a coarse correlated equilibrium that is better than the nash equilibrium. Performance evaluation shows that our proposed framework has a higher network throughput than other agricultural works and maintains stable sensor energy.

A Perfect Security Key Management Method for Hierarchical Wireless Sensor Networks in Medical Environments

Wireless sensor networks (WSNs) have developed during the past twenty years as a result of the accessibility of inexpensive, short-range, and simple-to-deploy sensors. A WSN technology sends the real-time sense information of a specific monitoring environment to a backend for processing and analysis. Security and management concerns have become hot topics with WSN systems due to the popularity of wireless communication channels. A large number of sensors are dispersed in an unmonitored medical environment, making them not safe from different risks, even though the information conveyed is vital, such as health data. Due to the sensor′s still limited resources, protecting information in WSN is a significant difficulty. This paper presents a hierarchical key management method for safeguarding heterogeneousWSNs on hybrid energy-efficient distributed (HEED) routing. In the proposed method, the Bloom scheme is used for key management and a pseudo-random number generator (PRNG) to generate keys in an efficient method to keep sensor resources. In addition, using cipher block chaining-Rivest cipher 5 (CBC-RC5) in this method achieved cryptography goals such as confidentiality. A comparison is made between the proposed and existing methods such as dynamic secret key management (DSKM) and smart security implementation (SSI) under the same circumstance to determine the performance of the new method. The data transmission in WSN consumes about 71 percent of a sensor′s energy, while encryption computation consumes only 2 percent. As a result, our method reduces the frequency with which data transmissions are made during the key management process. The simulation findings demonstrated that, in comparison to earlier techniques, the proposed method is significantly more secure, flexible, scalable, and energy-efficient. Our proposed method is also able to prevent classifications of node capture attacks.

A Bayesian Network for the Classification of Human Motion as Observed by Distributed Radar

In this article, a statistical model of human motion as observed by a network of radar sensors is presented where knowledge on the position and heading of the target provides information on the observation conditions of each sensor node. Sequences of motions are estimated from measurements of instantaneous Doppler frequency, which captures informative micromotions exhibited by the human target. A closed-form Bayesian estimation algorithm is presented that jointly estimates the state of the target and its exhibited motion class which are described by a hidden Markov model. To correct errors in the estimated motion class distribution introduced by faulty modeling assumptions, calibration of the probability distribution and measurement likelihood is performed by isotonic regression. It is shown, by modeling sensor observation conditions and by isotonic calibration of the measurement likelihood that a cognitive resource management system is able to increase classification accuracy by 5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\%}$</tex-math></inline-formula> –10 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\%}$</tex-math></inline-formula> while utilizing sensor resources in accordance with defined mission objectives.

Feedback-Assisted Automatic Target and Clutter Discrimination Using a Bayesian Convolutional Neural Network for Improved Explainability in SAR Applications

In this paper, a feedback training approach for efficiently dealing with distribution shift in synthetic aperture radar target detection using a Bayesian convolutional neural network is proposed. After training the network on in-distribution data, it is tested on out-of-distribution data. Samples that are classified incorrectly with high certainty are fed back for a second round of training. This results in the reduction of false positives in the out-of-distribution dataset. False positive target detections challenge human attention, sensor resource management, and mission engagement. In these types of applications, a reduction in false positives thus often takes precedence over target detection and classification performance. The classifier is used to discriminate the targets from the clutter and to classify the target type in a single step as opposed to the traditional approach of having a sequential chain of functions for target detection and localisation before the machine learning algorithm. Another aspect of automated synthetic aperture radar detection and recognition problems addressed here is the fact that human users of the output of traditional classification systems are presented with decisions made by “black box” algorithms. Consequently, the decisions are not explainable, even to an expert in the sensor domain. This paper makes use of the concept of explainable artificial intelligence via uncertainty heat maps that are overlaid onto synthetic aperture radar imagery to furnish the user with additional information about classification decisions. These uncertainty heat maps facilitate trust in the machine learning algorithm and are derived from the uncertainty estimates of the classifications from the Bayesian convolutional neural network. These uncertainty overlays further enhance the users’ ability to interpret the reasons why certain decisions were made by the algorithm. Further, it is demonstrated that feeding back the high-certainty, incorrectly classified out-of-distribution data results in an average improvement in detection performance and a reduction in uncertainty for all synthetic aperture radar images processed. Compared to the baseline method, an improvement in recall of 11.8%, and a reduction in the false positive rate of 7.08% were demonstrated using the Feedback-assisted Bayesian Convolutional Neural Network or FaBCNN.

Fully Adaptive Radar for Multiple Target Tracking

This work focuses on the development of a fully adaptive radar multiple target tracking (FAR-MTT) model and demonstrates its benefit over static parameter selections. The FAR-MTT framework is an extension of the fully adaptive radar (FAR) framework for single-target tracking (STT). In transitioning FAR-STT concepts to FAR-MTT, the focus was on developing a multiple target Fisher information matrix to capture the effects of multiple targets on the radar measurements, and on developing a robust but straightforward optimization scheme/objective function in the FAR executive processor. Simulation and experimental examples are presented to demonstrate the performance of the FAR-MTT approach. Compared to the fixed parameter case, the FAR-MTT technique is capable of obtaining specified tracking performance for each target and ensuring target separation while reducing sensor resource usage in a variety of multiple target environments. In Monte Carlo testing the method resulted in track merges 28% of the time compared with 50% of the time for a static parameter approach.

Multi-Sensor Scheduling Method Based on Joint Risk Assessment with Variable Weight.

In multi-sensor cooperative detection systems, to reduce target threat risk caused by attack tasks and target loss risk induced by uncertain environmental factors, this paper proposes a multi-sensor scheduling method based on joint risk assessment with variable weight. Firstly, considering the target state and prior expert experience of sensor scheduling, this paper gives a new scheme of target threat risk. Then, by combining the given target threat risk and the target loss risk, this paper constructs a joint risk model to meet the diversity of risk assessment. Secondly, a variable-weighted joint risk assessment model is given based on the adaptive weight of target loss risk and target threat risk, and the optimization problem of multi-sensor scheduling is described to minimize the multi-step prediction of the variable-weighted joint risk model. Finally, this paper relaxes above the non-convex optimization problem as a subconvex problem and designs the scheme of multi-sensor scheduling, improving the rapidity and optimization of the sensor scheduling solution. The simulation results show that the proposed method can adaptively schedule sensors and accurately track targets by using minimum sensor resources.

Exploiting Deep Reinforcement Learning for Coverage Maximization and Cost Minimization in Air Quality Monitoring Systems

Air-quality monitoring is highly desired in modern life, where environmental problems have become more serious. Such a task requires continuous surveillance over large urban areas, which is costly both in infrastructure and sensor resources. Hence it gives rise to Vehicular Mobile Networks (VMNs), in which mobile vehicles play the role of sensor devices and constantly monitor the area. However, with extensive constraints, the optimization of both maximizing the coverage and minimizing the sensory costs is vastly challenging. In this research, we resolve the problem in terms of a learning process. Applying deep reinforcement learning, we outperform more than 1.65% in terms of coverage, compared to common setups while remaining considerably small sensory costs in terms of sensor activation rate. We conduct extensive experiments for a better understanding of the behavior of the deep reinforcement learning model.

Performance Adjustable Event-Triggered Synchronization Policies to Nonlinear Multiagent Systems

This article designs performance adjustable event- and self-triggered policies for nonlinear multiagent systems with controller output fluctuations. In the provided event-triggered mechanism, a performance adjustable function is introduced, which can freely adjust the system sampling frequencies and state convergence rates. In the proposed self-triggered policy, continuous measurement error monitoring and neighbors’ event listening are avoided, thereby further saving system sensor resources. Furthermore, under a unified framework, the controllers’ inherent nonlinearities and the influence of data quantization in digital communication networks are studied. In comparison with the previous event-triggered sampling schemes, the developed policies do not only study the multiagent coordinated control in terms of system sampling performance and state convergence performance but are also suitable for the scenarios with controller output fluctuations. Numerical simulation results verify the effectiveness and superiority of the proposed policies.

Multi-sensor Resource Management and Intelligent Target Tracking Strategy Generation and Evaluation Based on Ant Colony Algorithm

This paper studies the multi-sensor resource management and intelligent tracking strategy generation for different targets. Firstly, the multi-sensor resource management methods are analyzed and compared with each other to specify the planning and information theories as the basis for this research. Then, a functional model is established according to the sensor resource updates, target visibility analysis, sensor performance analysis and sensor-to-target assignments. And the multi-sensor intelligent target tracking workflow is designed based on the function model. Thirdly, the corresponding multi-sensor intelligent target tracking strategy generation is designed based on ant colony algorithm. Finally, the evaluation analysis on the strategy generation is completed. This study provides a reference for the future studies on multi-sensor intelligent target tracking and multi-sensor system design.

Aggregation-Scheduling Based Mechanism for Energy-Efficient Multivariate Sensor Networks

Recently, the world has witnessed a technology revolution in many sectors and fields with the aim to enhance the quality of life and the human security. Particularly, the integration of small sensing devices into a wide range of objects has enabled the deployment of new services and applications that made our lives smarter and safety. With such sensing-based technology, we can adequately monitor, access harsh environments, understand various phenomena and make a decision accordingly. However, sensing-based applications face several challenges beginning from the data collection at the sensors themselves until the decision-making process at the end-user. In this study, we focus on the energy conservation as major challenge in sensor applications due to the limited sensor resources that are not always possible to be replaced or recharged. We propose an aggregation-scheduling based mechanism called as AGING for Energy-Efficient multivariate sensor networks (MSN). Mainly, AGING is based on the cluster network scheme and consists into two phases: aggregation and scheduling. The aggregation phase is applied at each node and aims to reduce the huge amount of data sent periodically to the cluster-head (CH) based on a user-defined score table and a multi-aggregation mechanism. The second phase is applied at CH and uses a new scheduling strategy to switch nodes generating similar data into sleep/active modes; the CH first converts the correlated nodes into a graph before applying a coloring-map algorithm and a scheduling strategy to select the set of active nodes in next periods. Through simulations on real sensor data, we show the relevance of our mechanism in terms of saving the node energies and prolong the network lifetime compared to other existing techniques.

Data-Driven Surrogate Model and Admissible-Region-Based Orbital Uncertainty Propagation

With fast-growing space objects but limited sensor resource, optical-sensor-based short-arc uncertainty propagation is necessary for space object tracking. Recently, the admissible-region-based orbit initialization method has been shown to be effective for uncertainty propagation when the number of observations is very small. However, it is challenging to describe the uncertainty distribution in the admissible region. In this paper, a data-driven kriging method is combined with the admissible region to build an accurate surrogate model for orbital uncertainty propagation instead of using the Monte Carlo method directly. In addition, an active learning strategy is utilized to choose sample data from the admissible region more efficiently for incrementally building the surrogate model. The main advantage of the kriging method is that no a priori probability density function needs to be assumed for the uncertainty distribution. Two numerical examples including a low Earth orbit and a geosynchronous Earth orbit propagation are used to demonstrate the effectiveness of the kriging model. It compares favorably to the polynomial chaos-based uncertainty propagation in terms of accuracy. It is also shown that the proposed method can achieve close performance to the Monte Carlo results but with much less sampling points and higher computational efficiency.

Distributed Optimal Control for HVAC Systems Adopting Edge Computing—Strategy, Implementation, and Experimental Validation

Internet of Things (IoT) has found increasing applications in industry, including the building automation field. Edge computing, a distributed computing paradigm using the computing resources of edge devices that are close to the sources of data, is an effective means for dealing with the network traffic caused by the centralized structure and rapid growth of IoT devices. This article proposes a distributed optimal control strategy for building heating, ventilation, and air conditioning systems adopting edge computing. The proposed distributed optimal control strategy is designed for the deployment on the smart sensor nodes in the IoT-enabled field control networks of building automation systems. A complex optimization task with high computational complexity is decomposed into several simple tasks, which can be handled by coordinating the agents implemented on the sensor nodes. In this manner, the computing resources of smart sensors are used collectively to optimize the subsystem operation locally. Compared with centralized optimal control strategies, the distributed form of the proposed strategy allows better generality and flexibility. A hardware-in-the-loop simulator is constructed as a realistic test environment for real control devices. Being implemented on a wireless IoT sensor network integrated in the simulator, the applicability and the performance of the proposed strategy are validated and evaluated. The experience and test results show that the IoT sensing network has the capacity to implement the distributed optimal control strategy and handle the decomposed optimization tasks effectively. The energy performance of the proposed distributed optimal control strategy is almost the same as that of the perfect solutions.

Characterization and Costs of Integrating Blockchain and IoT for Agri-Food Traceability Systems

An increasing amount of research focuses on integrating the Internet of Things and blockchain technology to address the requirements of traceability applications for Industry 4.0. However, there has been little quantitative analysis of several aspects of these new blockchain-based traceability systems. For instance, very few works have studied blockchain’s impact on the resources of constrained IoT sensors. Similarly, the infrastructure costs of these blockchain-based systems are not widely understood. This paper characterizes the resources of low-cost IoT sensors and provides a monetary cost model for blockchain infrastructure to support blockchain-based traceability systems. First, we describe and implement a farm-to-fork case study using public and private blockchain networks. Then, we analyze the impact of blockchain in six different resource-limited IoT devices in terms of disk and memory footprint, processing time, and energy consumption. Next, we present an infrastructure cost model and use it to identify the costs for the public and private networks. Finally, we evaluate the traceability of a product in different scenarios. Our results showed that low-cost sensors could directly interact with both types of blockchains with minimal energy overhead. Furthermore, our cost model showed that setting a private blockchain infrastructure costs approximately the same as that managing 50 products on a public blockchain network.

Multi-sensor target allocation model based on NC-PSO

Traditional multi-sensor target allocation often has problems such as slow algorithm iteration and low solution quality. In response to this problem, this paper proposes the NC-PSO algorithm, so that the particles can jump out of the local optimum and increase the convergence speed. At the same time, on the basis of the improved algorithm, the constraint of the target threat degree is added, the interval analytic hierarchy process is used to estimate the target threat, and the allocation principle is optimized, so that the use of sensor resources is better and the allocation is more reasonable. The simulation results prove that NC-PSO can allocate targets to multiple sensors faster and better.

Preliminary Report on the Train the Brain Project, Part I: Sensorimotor Neural Correlates of Anterior Cruciate Ligament Injury Risk Biomechanics.

Anterior cruciate ligament injury commonly occurs via noncontact motor coordination errors that result in excessive multiplanar loading during athletic movements. Preventing motor coordination errors requires neural sensorimotor integration activity to support knee-joint neuromuscular control, but the underlying neural mechanisms driving injury-risk motor control are not well understood. To evaluate brain activity differences for knee sensorimotor control between athletes with high or low injury-risk mechanics. Case-control study. Research laboratory. Of 38 female high school soccer players screened, 10 were selected for analysis based on magnetic resonance imaging compliance, injury-risk classification via 3-dimensional biomechanics during a drop vertical jump, and matching criteria to complete neuroimaging during knee motor tasks. Peak knee-abduction moment during landing was used for group allocation into the high (≥21.74 newton meters [Nm], n = 9) or low (≤10.6 Nm, n = 11) injury-risk classification (n = 11 uncategorized, n = 7 who were not compliant with magnetic resonance imaging). Ten participants (5 high risk, 5 low risk) with adequate data were matched and compared across 2 neuroimaging paradigms: unilateral knee-joint control and unilateral multijoint leg press against resistance. Athletes with high injury-risk biomechanics had less neural activity in 1 sensory-motor cluster for isolated knee-joint control (precuneus, peak Z score = 4.14, P ≤ .01, 788 voxels) and greater brain activity for the multijoint leg press in 2 cognitive-motor clusters: the frontal cortex (peak Z score = 4.71, P < .01, 1602 voxels) and posterior cingulate gyrus (peak Z score = 4.43, P < .01, 725 voxels) relative to the low injury-risk group. The high injury-risk group's lower relative engagement of neural sensory resources controlling the knee joint may elevate demand on cognitive motor resources to control loaded multijoint action. The neural activity profile in the high injury-risk group may manifest as a breakdown in neuromuscular coordination, resulting in elevated knee-abduction moments during landing.

Rational inattention in mice.

Behavior exhibited by humans and other organisms is generally inconsistent and biased and, thus, is often labeled irrational. However, the origins of this seemingly suboptimal behavior remain elusive. We developed a behavioral task and normative framework to reveal how organisms should allocate their limited processing resources such that sensory precision and its related metabolic investment are balanced to guarantee maximal utility. We found that mice act as rational inattentive agents by adaptively allocating their sensory resources in a way that maximizes reward consumption in previously unexperienced stimulus-reward association environments. Unexpectedly, perception of commonly occurring stimuli was relatively imprecise; however, this apparent statistical fallacy implies “awareness” and efficient adaptation to their neurocognitive limitations. Arousal systems carry reward distribution information of sensory signals, and distributional reinforcement learning mechanisms regulate sensory precision via top-down normalization. These findings reveal how organisms efficiently perceive and adapt to previously unexperienced environmental contexts within the constraints imposed by neurobiology.

Varibox encryption algorithm : The new generation of hybrid security measure for the era of quantum computation

The continual proliferation and evolution of the Internet of Things (IoT) domain is posing serious security threats to the whole network. In IoT, the insecure communication channel between sensors and the Internet can lead to malicious penetrations. Poor computational resources in traditional sensors critically constraint and impact the execution of conventional security algorithms among IoT devices. Consequently, lightweight security algorithms turn out to be a feasible alternative. To keep up with the existent Internet security level, this work proposes a new security scheme that employs a unique methodology based on a one-way counter value hashing table i.e. VariBox. This scheme integrates modular arithmetic applied on Unicode UTF-16 encoding standards coupled with AES and RSA algorithms. The proposed methodology effectively offers i) low computational overhead i.e. overall processing time of 0.1 seconds (VariBox processing time 0.003 seconds) and ii) enhances the overall security of the IoT data transfer model. Once this model is employed, the overall spectrum of potential brute force solutions will be rendered computationally expensive and infeasible to be calculated within a finite time period.

Disjoint multipath RPL for QoE/QoS provision in the Internet of Multimedia Things

RPL is the IETF standardized IPv6 Routing Protocol for Low-power and lossy networks (LLNs). The major effort is therefore made on handling low data rate traffic. Meanwhile, the Internet of Multimedia Things emerged as one hot topic of the Internet of Things. In LLNs, providing a descent quality of service (QoS) along with a user quality of experience (QoE) for multimedia applications is challenging. High bandwidth and significant computation capabilities are necessary while LLNs are very resource constrained. To raise the available bandwidth to accommodate high data rate applications, we propose to simultaneously transmit a flow on multiple disjoint paths. To do so, we exploit the DODAG structure, already maintained by RPL, to build disjoint paths without incurring extra overhead. To reduce the amount of data to send while considering the low computation resources of video sensors, we propose to apply a priority-based low-complexity encoding scheme on the captured pictures. Highest priority data can be replicated on more than one path. Our conducted experiments using simulations as well as a real testbed show that the best QoS and QoE are obtained when multiple paths are used along with the replication of highest priority data packets.

Deception Attacks on Remote Estimation with Disclosure and Disruption Resources

The problem of deception attacks utilizing both disclosure and disruption resources in the multiple types of sensors setting against remote state estimation is investigated in this paper. A group of sensors, including reliable and unreliable ones, measure system states and transmit innovations to a remote fusion center via wireless channels, wherein a malicious attacker can modify the data packets of unreliable sensors. By virtue of disclosure resources of reliable sensors and disruption resources of unreliable ones, a stealthy linear attack strategy is given, which can deceive the anomaly detectors and degrade the estimation performance. We obtain feasible criteria under which such attack policies can bypass existing detectors. The evolution of estimation error covariance at the remote end under the attacks is analyzed. Then, a closed-form expression of the optimal linear deception attack maximizing estimation error covariance is derived. Moreover, extended results on the designed attack strategies and attackers with extra measurements are provided. Finally, two illustrative examples with stable and unstable processes are given to verify the effectiveness of proposed methods.