Mission-critical Sensor Research Articles

Blockchain technology and internet of things: review, challenge and security concern

<span>Blockchain (BC) has received high attention from many researchers recently because it has decentralization, trusted auditability, and transparency as its main properties. BC has contributed fundamentally to the development of applications like cryptocurrencies, health care, the internet of things (IoT), and so on. The IoT is envisioned to include billions of pervasive and mission-critical sensors and actuators connected to the internet. This network of smart devices is expected to generate and have access to vast amounts of information, creating unique opportunities for new applications, but significant security and privacy issues emerge concurrently because it does not contain robust security systems. BC provides many services like privacy, security, and provenance to the systems that depends on. This research includes analyzing and a comprehensive review of BC technologies. Moreover, the proposed solutions in academia with the methodologies that used to integrate blockchain with IoT are presented. Also, the types of attacks on blockchain are collected and classified. Furthermore, the main contributions and challenges that are included in the literature are explored, then the relevant recommendations for solving the explored challenges are proposed. In conclusion, the integration of BC with IoT could produce promising results in enhancing the security and privacy of IoT environment.</span>

THWSN: Enhanced Energy-Efficient Clustering Approach for Three-Tier Heterogeneous Wireless Sensor Networks

Heterogeneous wireless sensor networks (HWSNs) provide realistic application design capabilities. The sensor nodes (SNs) are arranged in groups or clusters. Each cluster has a cluster head (CH). The CH accumulates the sensed data from its SNs to be sent to a base station (BS). SNs have been positioned with batteries, which cannot be replaced. Energy consumption is a major concern for WSNs. In order to achieve efficient mission-critical sensor networks, it is necessary to develop energy-efficient routing protocols. We suggest an innovative and inventive approach to design an enhanced energy-efficient clustering approach (EEECA) to increase the network lifetime and reduce the energy consumption in HWSNs. The proposed protocol is based on the three-tier nodes, namely, standard, intermediate, and advanced nodes. In the clustering procedure, we consider various parameters for selecting the CH, namely, the initial energy of the SNs, the number of alive/dead nodes, and the remaining energy. Extensive performance parameters show that the proposed EEECA-THWSN extends the network lifetime by 25% and decreases the network’s energy consumption. The proposed protocol improves the energy efficiency of EEECA-THWSN and significantly increases the network throughput, network lifetime, and network stability.

Trust-Based Intelligent Routing Protocol with Q-Learning for Mission-Critical Wireless Sensor Networks.

Mission-critical wireless sensor networks require a trustworthy and punctual routing protocol to ensure the worst-case end-to-end delay and reliability when transmitting mission-critical data collected by various sensors to gateways. In particular, the trustworthiness of mission-critical data must be guaranteed for decision-making and secure communications. However, it is a challenging issue to meet the requirement of both reliability and QoS in sensor networking environments where cyber-attacks may frequently occur and a lot of mission-critical data is generated. This study proposes a trust-based routing protocol that learns the trust elements using Q-learning to detect various attacks and ensure network performance. The proposed mechanism ensures the prompt detection of cyber threats that may occur in a mission-critical wireless sensor network and guarantees the trustworthy transfer of mission-critical sensor data. This paper introduces a distributed transmission technology that prioritizes the trustworthiness of mission-critical data through Q-learning results considering trustworthiness, QoS, and energy factors. It is a technology suitable for mission-critical wireless sensor network operational environments and can reliably operate resource-constrained devices. We implemented and performed a comprehensive evaluation of our scheme using the OPNET simulator. In addition, we measured packet delivery rates, throughput, survivability, and delay considering the characteristics of mission-critical sensor networks. The simulation results show an enhanced performance when compared with other mechanisms.

IEEE Access Special Section Editorial: Mission-Critical Sensors and Sensor Networks (MC-SSN)

Mission-critical sensors and sensor networks (MC-SSN) have been applied to missions such as battlefield, border patrol, search and rescue, critical structure monitoring, and surveillance. To support critical missions, sensors and sensor networks need to be flexible and interactive and continuously work despite limited bandwidth, intermittent connectivity, and with a large number of devices on the network. Sometimes, humans will be the elements within mission-critical sensors and sensor networks that are most vulnerable to deception, and humans will be handicapped when they are concerned about the received information from the network is not trustworthy, even if their concern is misplaced. In MCSSN, the advantages of linking multiple electronic support measures and electronic attack assets to achieve improved capabilities across a networked mission-critical force have yet to be quantified. Algorithms are sought for fused and/or coherent cross-platform radio frequency (RF) sensing. The MC-SSN algorithms should be capable of utilizing RF returns from multiple aspects in time-coordinated sensors and sensor networks. Such adaptation, management, and reorganization of information sources, devices, and networks must be accomplished almost autonomously in order to avoid imposing additional burdens on the humans, and without much reliance on support and maintenance services. Moreover, humans, under extreme cognitive and physical stress, will be strongly challenged by the massive complexity of the MC-SSN and the information it will provide and carry. Advances in technologies that capitalize on the benefits of the MC-SSN have to assist humans in making effective use of this massive, complex, confusing, and potentially deceptive ocean of information while taking into account the ever-changing mission. New approaches and low-complexity algorithms are expected to enable MC-SSN to automatically manage and effect risk and uncertainty in a highly deceptive, mixed cooperative/adversarial, information-centric environment.

Energy-Efficient Heterogeneous Optimization Routing Protocol for Wireless Sensor Network

A wide range of applications include in Wireless Sensor Networks (WSNs), and it is being used extensively in data collection specifically to process the mission-critical tasks. The implementation of routing protocols of energy-efficient (EE) is one of the significant challenging jobs of Sensor Networks (MC-SSN) and Mission Critical Sensors. In hierarchical routing protocols, higher EE can reach when compared to the flat routing protocols. The network’s scheduling process doesn’t support enhanced balanced Energy-efficient network-integrated super-heterogeneous (E-BEENISH), which discusses earlier. An Energy. Energy efficient Time scheduling based particle swarm optimization unequal fault tolerance clustering protocol (EE-TDMA-PSO-UFC) is proposed in this paper. Based on the distance parameter, an efficient cluster head (CH) is selected in this protocol. Owing to the unexpected failure of MCH (Master Cluster Head), an additional “CH” is chosen that is termed as Surrogate cluster head (SCH) for the restoration of network’s connectivity in the protocol of PSO-UFC. Based on TDMA (Time Division Multiple Access) protocols, the consumption of Energy. Energy is reduced with the allocation of timeslots during transmission of data. Using the technique of EE-TDMA-PSOUFC, the network’s lifespan improves than CEEC and E-BEENISH protocols according to the assessment of simulation results.

Intelligent Resource Collaboration in Mobile Target Tracking Oriented Mission-Critical Sensor Networks

Mobile target tracking-oriented sensor networks are a special kind of Mission-critical Sensor Networks (MCSN), in which the various missions with the diverse priorities exist. However, it is challenging to achieve real time tracking while keeping the MCSN a long life time with limited energy provision in a complicated environment. In this paper, we develop a collaborative perception and intelligent scheduling scheme, which jointly optimizes the system responding latency and tracking accuracy with the constraint of the available energy. A new hierarchical architecture is proposed to realize the coupled function of perception and computation. In particular, the multi-node collaborative perception scheme is applied to obtain the excellent sensing capacity, and the Unmanned Aerial Vehicles (UAVs) play as the edge nodes to provide the computing service for those resource-constrained sensor nodes. To reach the sustained target tracking, we propose an intelligent tracking policy by exploiting the deep deterministic policy gradient (DDPG) method. Simulation results demonstrate that the proposed intelligent collaboration scheme can improve the tracking accuracy by 45.5% compared with the random selection scheme. The system cost is also reduced approximately by 17.3% while guaranteeing the tracking accuracy.

Two-Dimensional Direction-of-Arrival and Polarization Parameter Estimation Using Parallel Co-Prime Polarization Sensitive Array

Target detection is critical in many mission critical sensors and sensor network (MC-SSN) applications. For target detection in complicated electromagnetic environment, DOA estimation using polarization sensitive array (PSA) has been receiving increased attentions. In this paper, we propose the parallel co-prime polarization sensitive array (PCP-PSA) which consists of the cocentered orthogonal dipole triads (CODTs) to estimate two-dimensional direction-of-arrival (2D DOA) and polarization parameters. The degrees of freedom (DOFs) have been extended due to the co-prime structure, so that the more signals can be detected and the estimation accuracy is improved. In order to reduce the computation complexity, we construct a new cross-covariance matrix based on the CODTs, which converts the two-dimensional DOA estimation into two independent one-dimensional DOA estimations. Then, the spatial smoothing-based multiple signal classification algorithm(MUSIC) and the sparse representation-based method are applied to estimate 2D DOA with only one-dimensional (1D) peak searching and 1D dictionary, respectively. Finally, the polarization parameters are estimated by using the cross-covariance matrix between components of electric field vector. Compared with previous PSA-based algorithms, the proposed algorithm based on PCP-PSA can solve the underdetermined 2D DOA and polarization parameters estimation problem and has better estimation accuracy. Theoretical analyses and simulation results verify the effectiveness of the proposed methods in terms of computational complexity and estimation accuracy.

Medium Access Control for Unmanned Aerial Vehicle Based Mission Critical Wireless Sensor Networks in 3D Monitoring Networks

In this paper, a novel medium access control (MAC) layer protocol is introduced for unmanned aerial vehicle (UAV)-based mission critical wireless sensor networks (MC-WSN), which is an important application of mission critical sensor and sensor networks (MC-SSN). The UAVs hovering in the three-dimensional monitoring networks are distributed in subsets according to their distance from the center station. UAVs in the same subset are contending for access and transmission, while each subset is allotted a slot based on adaptive time division multiple access (TDMA) scheme. As a multi-channel system, a novel channel allocation algorithm is presented for subset relay transmission period to optimize the performance of networks. The UAV returning period for charging is utilized in the protocol to enhance the transmission throughput and mitigate delay. Detection slot and position prediction algorithm are designed in the UAV returning period to improve the throughput and reduce the delay. The simulation results verify the improvement on throughput and delays of the proposed protocol for UAV-based MC-WSN.

A Joint Scheduling and Beamforming Scheme for RoF-Aided MC-SSN

Applied to harsh scenarios, the mission critical sensors and sensor network (MC-SSN) is expected to be of high capacity, density, and low cost. Both the industry and research community have a consensus on the need for energy and spectrum saving. Radio over fiber (RoF) system is promising for low latency and flexible system of high efficiency. In this paper, we consider the RoF-aided MC-SSN system serving the sensors with massive multiple-input multiple-output (MIMO) equipped. Based on this model, the base stations, relays, and other network elements can set up connections with each other via optical fiber or wireless links. Aiming to maximize the energy efficiency, this paper proposes a scheme including two aspects: 1) scheduling of the sensors based on the direction of arrival sensing of interference an 2) massive MIMO beamforming matrices generation based on a codebook. The numerical results show that the proposed scheduling and resource allocation method can achieve sufficient throughput with reduced power consumption for the RoF-aided MC-SSN system.

Adaptive Fuzzy Tree System for Target Tracking in Mission Critical Sensor Networks

The emergence of mobile sensors dramatically improves the availability of mission critical sensors and sensor networks (MC-SSN), enabling it to be utilized in more challenging tasks. However, current researches aimed to target tracking in mobile MC-SSN are very limited. This paper first proposes an adaptive fuzzy tree system (AFS) for target tracking in MC-SSN. To avoid oversimplifying the target mobile pattern, which is unrealistic in real tracking tasks, we introduce the target circle into the model considerations to add a bit of uncertainty about the target position. At each time step of the tracking process, strategy-selection layer will activate the pursuit strategy or the diffusion strategy for the specific situation, which makes the system more intelligent and can be applicable to various scenarios. The pursuit strategy is built by a two-layer fuzzy tree to select and mobilize sensors that have not detected target, while the diffusion strategy uses a fuzzy inference system to balance the density of detected sensors. And all the hyper-parameters are tuned by particle swarm optimization (PSO). We performed a large number of simulations with two target trajectories: line and irregular. The simulation results show that the AFS significantly outperforms the state-of-the-art algorithm. Moreover, it is highly robust to various target motion patterns, making it competent for a variety of real target tracking scenarios.

Image Data Augmentation for SAR Sensor via Generative Adversarial Nets

As a mission-critical sensor, SAR has been applied in environmental monitoring and battlefield surveillance; moreover, SAR target recognition is one of the most important applications of SAR technology. However, in practical applications, the number of samples available for training is relatively small, so the SAR target recognition can be regarded as a small sample recognition problem. One of the main directions to solve the small sample recognition problem is to realize the data augmentation. Therefore, a SAR image data augmentation method via Generative Adversarial Nets (GAN) is proposed in this paper. The method uses Wasserstein GAN with a gradient penalty (WGAN-GP) to generate new samples based on existing SAR data, which can augment the sample number in training dataset. Meanwhile, the sample selection filters are designed to extract the generated samples with high quality and specific azimuth, which can avoid the randomness of the data augmentation, and improve the quality of the newly generated training samples. The experiments based on MSTAR data show that, for three-class recognition problem, when the training sample is only 108, the proposed method can improve the recognition rate from 79% to 91.6%; and for ten-class recognition problem, when the training sample is only 360, the proposed method can improve the recognition rate from 57.48% to 79.59%. Compared with the traditional data linear generation method, the proposed method shows significant improvement on the quantity and quality of the training samples, and can effectively solve the problem of the small sample recognition.

Soil Moisture Retrieval Algorithm Based on TFA and CNN

With the development of mission critical sensors and sensor networks (MC-SSNs), using MC sensor for monitoring the changes of soil moisture has become convenient and real time. However, the method used to process the data obtained by the MC sensor directly affects the monitoring effect. Although the past research methods have achieved certain results, they all need to extract data features. In this paper, we propose the soil moisture retrieval algorithm with time–frequency analysis and convolutional neural network (CNN) based on ultra-wideband radar echoes, which do not need to build feature database in advance. The algorithm transforms the soil echoes into time–frequency (TF) distribution patterns and utilizes the CNN algorithm to classify soil echoes with different soil volumetric water contents (VWCs). Wigner–Ville (WV) distribution and Choi–Williams (CW) distribution are the two methods used for time–frequency transform; VGGNet and AlexNet are, respectively, applied to classify the TF patterns with different VWCs. We totally construct four soil moisture retrieval systems (WV-AlexNet, CW-AlexNet, WV-VGGNet, and CW-VGGNet), and the echoes of 27 soil water contents with different signal-to-noise ratios (SNRs) are studied. The simulation results with raw data show that the correct recognition rate of soil VWCs can reach 100% when the soil echoes are at 10 dB (SNR). The WV-AlexNet system has the best recognition performance among the four systems.

Energy-Efficient Multilevel Heterogeneous Routing Protocol for Wireless Sensor Networks

Wireless sensor networks (WSNs) are being applied widely for data collection, especially to carry out the mission-critical tasks. Therefore, one of the most challenging tasks of mission-critical sensors and sensor networks is the development of energy efficient (EE) routing protocols. Comparing with flat routing protocols, more EE can be achieved in hierarchical routing protocols. In this paper, we propose an enhanced balanced energy efficient network-integrated super-heterogeneous (E-BEENISH) routing protocol, by analyzing communication energy consumption of the clusters and a large range of energy levels in heterogeneous WSNs. E-BEENISH is based on weighted election probabilities of each node to become a cluster head according to the remaining energy and the distance from the sink to the node. Moreover, we also study the impact of the heterogeneity of nodes in terms of energy. Studying the sensitivity of our stable election protocol, we conclude heterogeneity parameters capturing energy imbalance in the network and find that the E-BEENISH yields the longer stability region for the suitable weight of energy and distance. Our results show by simulation that the E-BEENISH can improve system lifetime by an order of magnitude compared to obtained using current clustering protocols, which is crucial for many applications.

Soil Moisture Retrieval From UWB Sensor Data by Leveraging Fuzzy Logic

An ultra-wide-band radar module sensor for soil moisture monitoring field experiment is investigated in this paper. For this type of mission critical sensor, it is applied to collect the reflected signals from subsurface of bare soil and sand with different volumetric water contents (VWCs) data (which are calibrated by a time domain reflectometer). This problem is formulated as a mapping from the raw data to the physical parameter. The fuzzy logic algorithm is employed to track the trend of the time series data and after the forecasting becomes stable, the parameters of membership functions in the final iteration are extracted as templates and the VWC values are computed based on a recognition fashion. Two type of fuzzy logic systems (FLSs), namely, type-1 FLS and interval type-2 FLS are employed and compared under the root-mean-square-error. Finally, the accuracy of the soil moisture retrieval is also compared under the mean absolute deviation and the root mean square difference, which demonstrates the effectiveness of the algorithm.