With the surge of ubiquitous demand for high-complexity and quality mobile Internet-of-things (IoT) services, new cooperative relaying paradigms have emerged. Motivated by the long and unpredictable end-to-end communication in relay-aided IoT networks, there is a need to introduce novel modulation schemes for very low bit error rate (BER) communications. In this paper, a practical modulation mapping scheme has been proposed to reduce decoding errors. Specifically, a hybrid automatic repeat request (HARQ) system has been used with an intermediate relay to transfer a message from a source to a destination. The design of modulation mapping has been optimized by first formulating the objective as the quadratic assignment problem. Later, the solution to the mapping problem is provided using an iterative search method. To validate the proposed solution, extensive simulations have been performed in MATLAB. The results show that the proposed solution outperforms the conventional relay retransmission and the heuristic design approaches.

Irrigation operations in agriculture are one of the largest water consumers in the world, and it has been increasing due to rising population and consequent increased demand for food. The development of advanced irrigation technologies based on modern techniques is of utmost necessity to ensure efficient use of water. Smart irrigation based on computer vision could help in achieving optimum water-utilization in agriculture using a highly available digital technology. This paper presents a non-contact vision system based on a standard video camera to predict the irrigation requirements for loam soils using a feed-forward back propagation neural network. The study relies on analyzing the differences in soil color captured by a video camera at different distances, times and illumination levels obtained from loam soil over four weeks of data acquisition. The proposed system used this color information as input to an artificial neural network (ANN) system to make a decision as to whether to irrigate the soil or not. The proposed system was very accurate, achieving a mean square error (MSE) of 1.616 × 10 −6 (training), 1.004 × 10 −5 (testing) and 1.809 × 10 −5 (validation). The proposed system is simple, robust and affordable making it promising technology to support precision agriculture.

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Wireless sensor networks (WSNs) and their applications have received significantly interested in the last few years. In WSN, knowing an accurate path-loss model as well as packet delivery should be taken into account for the successful distribution of several nodes in the network. This paper presents a path-loss modeling and performance evaluation of the ZigBee wireless standard. Received signal strength indicator (RSSI) measurements were achieved in outdoor and indoor environments to derive the path-loss based on Log-Normal Shadowing Model (LNSM). The path-loss parameters such as standard deviation and path loss exponents were estimated over point-to-point ZigBee WSN. In addition, the variances of received RSSI values and standard deviation for these values have been investigated. Furthermore, the data packets received is measured practically. Results revealed that the LNSM can be estimated to reflect the channel losses in both outdoor and indoor environments for medical application. The data delivery was achieved successfully of 100% in outdoor which better than indoor due to multipath propagation and shadowing. Moreover, the data packets delivery of the current work outperformed previous work.