Least-Square Collaborative Beamforming Linear Array for Steering Capability in Green Wireless Sensor Networks

This paper presents a collaborative beamforming (CB) technique to organize the sensor node's location in a linear array for green wireless sensor network (WSN) applications. In this method, only selected clusters and active CB nodes are needed each time to perform CB in WSNs. The proposed least-square linear array (LSLA) manages to select nodes to perform as a linear antenna array (LAA), which is similar to and as outstanding as the conventional uniform linear array (ULA). The LSLA technique is also able to solve positioning error problems that exist in the random nodes deployment. The beampattern fluctuations have been analyzed due to the random positions of sensor nodes. Performances in terms of normalized power gains are given. It is demonstrated by a simulation that the proposed technique gives similar performances to the conventional ULA and at the same time exhibits lower complexity.     

A Green Clustering Protocol for Mobile Sensor Network Using Particle Swarm Optimization

Energy consumption of sensor nodes is one of the crucial issues in prolonging the lifetime of wireless sensor networks. One of the methods that can improve the utilization of sensor nodes batteries is the clustering method. In this paper, we propose a green clustering protocol for mobile sensor networks using particle swarm optimization (PSO) algorithm. We define a new fitness function that can optimize the energy consumption of the whole network and minimize the relative distance between cluster heads and their respective member nodes. We also take into account the mobility factor when defining the cluster membership, so that the sensor nodes can join the cluster that has the similar mobility pattern. The performance of the proposed protocol is compared with well-known clustering protocols developed for wireless sensor networks such as LEACH (low-energy adaptive clustering hierarchy) and protocols designed for sensor networks with mobile nodes called CM-IR (clustering mobility-invalid round). In addition, we also modify the improved version of LEACH called MLEACH-C, so that it is applicable to the mobile sensor nodes environment. Simulation results demonstrate that the proposed protocol using PSO algorithm can improve the energy consumption of the network, achieve better network lifetime, and increase the data delivered at the base station.     

Process Noise Parameters of Beamforming Green Nodes

Signals arrive out of phase at the intended receiver from collaborative beamforming (CB) nodes due to the instability in the output frequency signals of the universal software radio peripheral's (USRP) local oscillator (LO). These nodes including the target must synchronize their oscillator frequencies for coherent signal reception. In order to do this, frequencies and phases of the signals should be estimated in software defined radio (SDR) and smoothen with nonlinear filters such as the extended Kalman filter (EKF). The process noise parameters of the NI USRP-2920 nodes will have to be calculated and used with the EKF process noise covariance matrix. These nodes are green communication hardware devices where most of the hardware units are now software defined. This article uses the direct spectrum method to obtain the phase noise values at various frequency offsets of the NI USRP-2920 in order to calculate the power spectral density of fractional frequency fluctuation. By applying the power-law noise model to this obtained value, the generated white frequency noise and random walk frequency noise values are q1=1.9310-21 and q2=5.8610-18, respectively.     

Real-Time Frequency Estimation of Complex GMSK Signal of Green Communications Devices

Parameter estimation of signals of universal software radio peripheral (USRP) devices is crucial to solve the problem of phase offsets of received signals in distributed beamforming. For systems that will utilize the closed loop feedback algorithm where the receiver needs to send the received signal strength (RSS) values periodically to the beamforming node so as to take advantage of energy conservation, the frequency and phase of these signals should be estimated before smoothening by nonlinear filters. This article presents the estimation of the frequency offsets of a Gaussian minimum shift keying (GMSK) signal from N210 USRP devices in real time by using the Radix-2 fast Fourier transform (FFT) algorithm in GNURadio. For these green communications devices, most of the needed hardware parts have been software defined, thereby reducing the supposed energy consumption. The frequency offsets from reference carrier frequencies of 900 MHz and 2.4 GHz are less than 3 kHz each before the estimation, but the average offsets are 45 Hz and 100 Hz after the estimation, respectively. The high offset value experienced with the 2.4 GHz carrier was due to consistent interference from devices on that same frequency.     

Transmission Range-Aware Clustering for Green Cognitive Radio Ad Hoc Networks

The absence of network infrastructure and opportunistic spectrum access in cognitive radio ad hoc networks (CRAHNs) results in connectivity and stability problems. Clustering is known as an effective technique to overcome this problem. Clustering improves network performance by implementing a logical network backbone. Therefore, how to efficiently construct this backbone among CRAHNs is of interest. In this paper, we propose a new clustering algorithm for CRAHNs. Moreover, we model a novel cluster head selection function based on the channel heterogeneity in term of transmission ranges. To the best of our knowledge, this is the first attempt to model the channel heterogeneity into the clustering formation in cognitive radio networks. Simulation results show that the performance of clustering is significantly improved by the channel heterogeneity considerations.