Journal of Sensors

The harsh testing environments of underwater scenarios make it extremely hard to plan a reasonable routing protocol for Underwater Sensor Networks (UWSNs). The main challenge in UWSNs is energy confinement. It is needed to plan an energy effective scheme which increases the life span of the network and also reduces the energy usage in data transfer from supplier to sink. In this research, we present the design of a routing protocol known as Energy Harvesting in UWSN (EH-UWSN). EH-UWSN is a compact, energy efficient, and high throughput routing protocol, in which we present utilization of energy gaining with coordinating transfer of data packets through relay nodes. Through Energy Harvesting, the nodes are capable to recharge their batteries from the outside surrounding with the ultimate objective to improve the time span of network and proceed data through cooperation, along with restricting energy usage. At the sink node, the mixing plan applied is centered on Signal-to-Noise Ratio Combination (SNRC). Outcomes of EH-UWSN procedure reveal good results in terms of usage of energy, throughput, and network life span in comparing with our previous Cooperative Routing Scheme for UWSNs (Co-UWSN). Simulation results show that EH-UWSN has consumed considerably lesser energy when compared with Co-UWSN along with extending network lifetime and higher throughput at the destination.

The rapid advancement in the field of wireless sensor and cellular networks have established a rigid foundation for the Internet of Things (IoT). IoT has become a novel standard that incorporates various physical objects by allowing them to collaborate with each other. A large number of services and applications emerging in the field of IoT that include healthcare, surveillance, industries, transportation, and security. A service provider (SP) offers several services that are accessible through smart applications from any time, anywhere, and any place via the Internet. Due to the open nature of mobile communication and the Internet, these services are extremely susceptible to various malicious attacks, e.g., unauthorized access from malicious intruders. Therefore, to overcome these susceptibilities, a robust authentication scheme is the finest solution. In this article, we introduce a lightweight identity-based remote user authentication and key agreement scheme for IoT environment that enables secure access to IoT services. Our introduced scheme utilizes lightweight elliptic curve cryptography (ECC), hash operations, and XOR operations. The theoretical analysis and formal proof are presented to demonstrate that our scheme provides resistance against several security attacks. Performance evaluation and comparison of our scheme with several related schemes for IoT environment are carried out using the PyCrypto library in Ubuntu and mobile devices. The performance analysis shows that our scheme has trivial storage and communication cost. Hence, the devised scheme is more efficient not only in terms of storage, communication, and computation overheads but also in terms of providing sufficient security against various malicious attacks.

Drilling a high-pressure, high-temperature (HPHT) well involves many difficulties and challenges. One of the greatest difficulties is the loss of circulation. Almost 40% of the drilling cost is attributed to the drilling fluid, so the loss of the fluid considerably increases the total drilling cost. There are several approaches to avoid loss of return; one of these approaches is preventing the occurrence of the losses by identifying the lost circulation zones. Most of these approaches are difficult to apply due to some constraints in the field. The purpose of this work is to apply three artificial intelligence (AI) techniques, namely, functional networks (FN), artificial neural networks (ANN), and fuzzy logic (FL), to identify the lost circulation zones. Real-time surface drilling parameters of three wells were obtained using real-time drilling sensors. Well A was utilized for training and testing the three developed AI models, whereas Well B and Well C were utilized to validate them. High accuracy was achieved by the three AI models based on the root mean square error (), confusion matrix, and correlation coefficient (). All the AI models identified the lost circulation zones in Well A with high accuracy where the is more than 0.98 and is less than 0.09. ANN is the most accurate model with and . An ANN was able to predict the lost circulation zones in the unseen Well B and Well C with and and and , respectively.

The traditional methods cannot be used to meet the requirements of rapid and objective detection of meat freshness. Electronic nose (E-Nose), computer vision (CV), and artificial tactile (AT) sensory technologies can be used to mimic humans’ compressive sensory functions of smell, look, and touch when making judgement of meat quality (freshness). Though individual E-Nose, CV, and AT sensory technologies have been used to detect the meat freshness, the detection results vary and are not reliable. In this paper, a new method has been proposed through the integration of E-Nose, CV, and AT sensory technologies for capturing comprehensive meat freshness parameters and the data fusion method for analysing the complicated data with different dimensions and units of six odour parameters of E-Nose, 9 colour parameters of CV, and 4 rubbery parameters of AT for effective meat freshness detection. The pork and chicken meats have been selected for a validation test. The total volatile base nitrogen (TVB-N) assays are used to define meat freshness as the standard criteria for validating the effectiveness of the proposed method. The principal component analysis (PCA) and support vector machine (SVM) are used as unsupervised and supervised pattern recognition methods to analyse the source data and the fusion data of the three instruments, respectively. The experimental and data analysis results show that compared to a single technology, the fusion of E-Nose, CV, and AT technologies significantly improves the detection performance of various freshness meat products. In addition, partial least squares (PLS) is used to construct TVB-N value prediction models, in which the fusion data is input. The root mean square error predictions (RMSEP) for the sample pork and chicken meats are 1.21 and 0.98, respectively, in which the coefficient of determination () is 0.91 and 0.94. This means that the proposed method can be used to effectively detect meat freshness and the storage time (days).

This paper presents a device-free human detection method for using Received Signal Strength Indicator (RSSI) measurement of Wireless Sensor Network (WSN) with packet dropout based on ZigBee. Packet loss is observed to be a familiar phenomenon with transmissions of WSNs. The packet reception rate (PRR) based on a large number of data packets cannot reflect the real-time link quality accurately. So this paper firstly raises a real-time RSSI link quality evaluation method based on the exponential smoothing method. Then, a device-free human detection method is proposed. Compared to conventional solutions which utilize a complex set of sensors for detection, the proposed approach achieves the same only by RSSI volatility. The intermittent Karman algorithm is used to filter RSSI fluctuation caused by environment and other factors in data packets loss situation, and online learning is adopted to set algorithm parameters considering environmental changes. The experimental measurements are conducted in laboratory. A high-quality network based on ZigBee is obtained, and then, RSSI can be calculated from the receive sensor modules. Experimental results show the uncertainty of RSSI change at the moment of human through the network area and confirm the validity of the detection method.

Sentinel-2A/MSI (S2A) and Landsat-8/OLI (L8) data products present a new frontier for the assessment and retrieval of optically active water quality parameters including chlorophyll-a (Chl-a), suspended particulate matter (TSS), and turbidity in reservoirs. However, because of their differences in spatial and spectral samplings, it is critical to evaluate how well the sensors are suited for the seamless generation of the water quality parameters (WQPs). This study presents results from the retrieval of the WQP in a reservoir from L8 and S2A optical sensors, after atmospheric correction and standardization through band adjustment. An empirical multivariate regression model (EMRM) algorithmic approach is proposed for the estimation of the water quality parameters in correlation with in situ laboratory measurements. From the results, both sensors estimated Chl-a concentrations with of greater than 70% from the visible green band for L8 and a combination of green and SWIR-1 bands for S2A. While the NMSE% was nearly the same for both sensors in Chl-a estimation, the RMSE was <10 μg/L and >10 μg/L for L8 and S2A estimations of Chl-a, respectively. For TSS retrieval, L8 outperformed S2A by 31% in accuracy with from L8’s red, blue, and green bands, as compared to from S2A’s red and NIR bands. The RMSE were the same as for Chl-a, and the NMSE% were both in the same range. Both sensors retrieved turbidity with high and nearly equal accuracy of from the visible and NIR bands, with equal RMSE at <10% NTU and NMAE% from S2A being higher by more than 30% as compared to L8’s NMAE% at 15%. The study concluded that the higher performance accuracy of L8 is attributed to its higher SNR and spectral bandwidth placement as compared to S2A bands. Comparatively, S2A overestimated Chl-a and turbidity but performed equally well compared to OLI in the estimation of TSS. The results show that while absolute accuracy of retrieval of the WQPs still requires improvements, the developed algorithms are broadly able to discern the biooptical water quality in reservoirs.