Abstract This paper studies the optimization of energy efficiency in coexisting wireless body area networks (WBANs). A solution based on combining a naive Bayesian classifier with the Hungarian algorithm is proposed to improve link transmission energy efficiency. The solution is implemented in the following three steps: Firstly, the interference from surrounding WBANs is identified based on a naive Bayesian classifier considering the distance among WBANs, the residual energy of the sensor nodes, and the transmission power of the sensor nodes. Secondly, the signal‐to‐interference plus noise ratio is determined according to the results of the naive Bayesian classifier. Thirdly, the time slots are allocated adaptively by using the Hungarian algorithm to maximize the overall energy efficiency. The simulation results show that the scheme can improve the overall energy efficiency of the WBAN significantly while ensuring quality of service. In comparison with the iterative algorithm and the PONF algorithm, the proposed scheme has obvious advantages in improving energy efficiency.

A dual optimization mechanism is proposed to improve energy efficiency in coexisting wireless body area networks (WBANs). The first optimization uses a naive Bayesian classifier to establish a minimization interference model of the current WBAN to improve energy efficiency. The second optimization is achieved by allocating time slots based on the Hungarian algorithm to improve energy efficiency further. The simulation results show that the scheme can maximize the overall energy efficiency of the WBAN significantly while ensuring the Quality of Service (QoS).

The interference of the uplink of coexisting wireless body area networks is studied in this article. When multiple WBANs working in the same channel perform data transmission simultaneously, their uplinks will be interfered with by multiple transmission links of adjacent WBANs, which leads to data packet loss and data transmission failure. Therefore, mitigating interference and maximizing throughput are the main goals of this article. First, the graph coloring algorithm is used to analyze the interference relationship among WBANs and a mathematical model of throughput maximization is formulated. Then, the time slot reallocation algorithm is proposed to achieve node-level optimization but with high complexity. The simulation results show that the proposed scheme can achieve a good packet reception rate and throughput for coexisting WBANs.

Abstract The efficient selection of cluster head nodes for the transmission of the data to the sink node is the major requirement of the wireless body area network. The present work deals with the efficient selection of the cluster head node and uniform rotation of the cluster head based on the four important parameters, i.e. residual energy of the node, mean value of the Euclidean distance, priority level based on data sensitivity, and device physical capability. The proposed energy‐efficient Bayesian clustering algorithm uses naïve Bayesian approach for the appropriate selection of the cluster head node. The selected cluster head node collects the data from the sensor nodes in each round. The collected data is first aggregated and then transmitted to the base station. In the steady‐state phase, the proposed algorithm follows energy‐analysed‐TDMA based energy‐efficient data transmission method which further reduces the energy consumption as compared to the standard LEACH protocol. The performance of the proposed protocol is compared to the existing protocol on the MATLAB simulator. The proposed scheme increases the overall lifetime of the network by approximately 1.5 times and also reduces the average energy consumption of the network.

In this paper, we propose a null space-based precoding scheme for secondary transmission in a cognitive radio multiple-input multiple-output (CR-MIMO) network under the assumption that time-division-duplex is employed by the primary transmission. First, the secondary transmitter periodically senses the transmitted signals from the primary users and estimates the corresponding covariance matrix. Then, subspace techniques are utilized to estimate the noise subspace of this covariance matrix, and the dimension of the noise subspace is estimated using information theory criteria (AIC or MDL). Finally, the obtained null space is used as the precoding matrix for the secondary transmission, which effectively avoids the interference induced by the secondary transmission at the primary users. Moreover, the achievable capacity of the secondary MIMO channel by the proposed scheme is derived. Simulations are performed to show the efficacy of the proposed scheme.