Providing an accurate prediction of water quality parameters for improved water quality management is a topical issue in the aquaculture industry. Conventional prediction methods have shown different challenges like a poor generalization, poor prediction accuracy, and high time complexity. Aiming at these challenges, a novel hybrid prediction model with ensemble empirical mode decomposition (EEMD) and deep learning (DL) long-short term memory (LSTM) neural network is proposed in this paper. In this innovative hybrid EEMD-DL-LSTM model, firstly, the integrity of the datasets is enhanced by applying moving average filtering and linear interpolation techniques of water quality parameter datasets pre-treatment. Secondly, the measured real sensor water quality parameters dataset is decomposed with the aid of the EEMD algorithm into disparate IMFs and a corresponding residual item. Thirdly, a multi-feature selection process is applied to make a careful selection of a strongly correlated group of IMFs with the measured real water quality parameter datasets and integrate them as inputs to the DL-LSTM neural network. The presented model is built on water quality sensor data collected from an Abalone farm in South Africa. The performance of the novel hybrid prediction model is validated by comparing the results against the real datasets. To measure the overall accuracy of the novel hybrid prediction model, different statistical indices, namely the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), are used.

Internet of Things (IoTs) era is expected to empower all aspects of Intelligent Transportation System (ITS) to improve transport safety and reduce road accidents. US Federal Communication Commission (FCC) officially allocated 75 MHz spectrum in the 5.9 GHz band to support vehicular communication. The authors studied the application of cognitive radio (CR) technology to IoVs to increase the spectra opportunities for vehicular communication, especially when the allocated 75 MHz is not enough due to high demands because of the increasing number of connected vehicles as already foreseen in the near era of IoVs. They proposed a novel CR Assisted Vehicular NETwork (CRAVNET) framework which empowers CR enabled vehicles to make opportunistic usage of licensed spectrum on the highways. They also developed a novel cooperative three‐state spectrum sensing and allocation model which makes CR vehicular secondary units aware of additional spectrum resources opportunities on their current and future positions, and applies optimal sensing node allocation algorithm to guarantee timely acquisition of the available channels within a limited sensing time. The results of the theoretical analyses and simulation experiments have demonstrated that the proposed model can significantly improve the performance of a cooperative spectrum sensing, and provide vehicles with additional spectrum opportunities.

Dissolved oxygen (DO) concentration is a vital parameter that indicates water quality. We present here DO short term forecasting using time series analysis on data collected from an aquaculture pond. This can provide the basis of data support for an early warning system, for an improved management of the aquaculture farm. The conventional forecasting approaches are commonly characterized by low accuracy and poor generalization problems. In this article, we present a novel hybrid DO concentration forecasting method with ensemble empirical mode decomposition (EEMD)-based LSTM (long short-term memory) neural network (NN). With this method, first, the sensor data integrity is improved through linear interpolation and moving average filtering methods of data preprocessing. Next, the EEMD algorithm is applied to decompose the original sensor data into multiple intrinsic mode functions (IMFs). Finally, the feature selection is used to carefully select IMFs that strongly correlate with the original sensor data, and integrate into both inputs for the NN. The hybrid EEMD-based LSTM forecasting model is then constructed. The performance of this proposed model in training and validation sets was compared with the observed real sensor data. To obtain the exact evaluation accuracy of the forecasted results of the hybrid EEMD-based LSTM forecasting model, four statistical performance indices were adopted: mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Results are presented for the short term (12-h) and the long term (1-month) that are encouraging, indicating suitability of this technique for forecasting DO values.

With the envisioned era of internet of things, all aspects of Intelligent Transportation Systems will be connected to improve transport safety, relieve traffic congestion, reduce air pollution, enhance the comfort of transportation and significantly reduce road accidents. In internet of vehicles, regular exchange of current position, direction, velocity and so on, enables mobile vehicles to predict an upcoming accident and alert the human drivers in time or proactively take precautionary actions to avoid the accident. The actualisation of this concept requires the use of channel access protocols that can guarantee reliable and timely broadcast of safety messages. This study investigates the application of network coding concept to increase content of every transmission and achieve improved broadcast reliability with less number of retransmissions. In particular, the authors proposed Code Aided Retransmission‐based Error Recovery (CARER) scheme, introduced a request‐to‐broadcast/clear‐to‐broadcast (RTB/CTB) handshake to overcome hidden node problem and reduce packets collision rate. In order to avoid broadcast storm problem associated with the use of RTB/CTB packet in a broadcast transmission, they developed a rebroadcasting metric used to successfully select a vehicle to rebroadcast the encoded message. The performance of CARER protocol is clearly shown with detailed theoretical analysis and further validated with simulation experiments.

Eutrophication in fresh water has become a critical challenge worldwide and chlorophyll-a content is a key water quality parameter that indicates the extent of eutrophication and algae concentration in a body of water. In this paper, a forecasting model for the high accuracy prediction of chlorophyll-a content is proposed to enable aquafarm managers to take remediation actions against the occurrence of toxic algal blooms in the aquaculture industry. The proposed model combines the ensemble empirical mode decomposition (EEMD) technique and a deep learning (DL) long short-term memory (LSTM) neural network (NN). With this hybrid approach, the time-series data are firstly decomposed with the aid of the EEMD algorithm into manifold intrinsic mode functions (IMFs). Secondly, a multi-attribute selection process is employed to select the group of IMFs with strong correlations with the measured real chlorophyll-a dataset and integrate them as inputs for the DL LSTM NN. The model is built on water quality sensor data collected from the Loch Duart salmon aquafarm in Scotland. The performance of the proposed novel hybrid predictive model is validated by comparing the results against the dataset. To measure the overall accuracy of the proposed novel hybrid predictive model, the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) were used.