As a key technology, clustering has been an effective way in large-scale wireless sensor networks (WSNs) to extend the lifetime. However, the static cluster structure in most of the traditional method is formed without considering the development of the event. In this paper, we propose an Energy-Efficient Dynamic Clustering (EEDC) protocol for event monitoring applications in large scale WSN. In EEDC, a dynamic clustering method using Rough Fuzzy C-Means and Genetic algorithm (RFCM-GA) is designed. Firstly, the idea of fuzzy set and rough set in RFCM are used to form the overlapping cluster, which can guarantee the quality of coverage of the developing event. Secondly, we use GA to perform a parallel search in each cluster to find the optimal set of candidate cluster heads (CCHs). RFCM-GA can use its powerful global search capabilities and fast convergence speed to obtain the best clustering results. Simulation results demonstrate that EEDC has higher energy efficiency and prolongs the network lifetime compared to the existing approaches.

A unified error analysis of multi-antenna systems with orthogonal space-time block coding (OSTBC) using transmit antenna selection (TAS) over Nakagami-m channels in the presence of feedback delay and channel estimation error is presented, where the number of transmit antennas is arbitrarily selected. For imperfect channel state information (CSI), the Nakagami-m channel is modeled as the sum of Nakagami-m̂ channel and independent Gaussian channel error. By this model, accurate and closed-form approximate bit error rate (BER) expressions of TAS-OSTBC for M-ary quadrature amplitude modulation and M-ary phase-shift keying are derived by using the moment generating function of received SNR. With the approximate BER formula, the number of antennas to be selected for transmission can be adapted by minimizing the BER of the system with imperfect CSI. The diversity and coding gains of TAS-OSTBC with K receive antennas and M antennas selected from N transmit antennas for transmission are analyzed at high SNR. For noninteger mK, the upper and lower bounds of the diversity gain for different CSI and estimation error scenarios are derived. But for integer mK, the results indicate that the upper and lower bounds are equal achieving the full diversity order of mKN for perfect CSI, zero diversity order and partial diversity order of mMK for estimation error variance being fixed and being a decreasing function of average SNR, respectively. The coding gain is affected by M, N, K, Nakagami parameter m, code rate, modulation, and channel correlation coefficients. Simulation results verify the validity of the theoretical analysis and effectiveness of the adaptive antenna selection scheme.

Wideband spectrum sensing based on sub-Nyquist sampling is an attractive approach to advance dynamic spectrum sharing (DSS), which can improve frequency resource utilization while overcoming sampling bottlenecks. Under the Compressive Sensing (CS) framework, finding occupied subbands can be equivalent to computing the support set for the Multiple Measurement Vectors (MMV) problem. To guarantee the performance of joint support recovery in noisy environments, different kinds of prior information are required, one of which is sparsity, a time-varying parameter. To address the dependence of recovery performance on signal sparsity, this paper proposed a two-step scheme for blind wideband spectrum sensing using a Modulated Wideband Converter (MWC) sub-Nyquist sampling front-end. The scheme first adopts the model order selection (MOS) method to estimate sparsity from the compressed covariance matrix, and then uses the estimates to dynamically adjust joint support recovery. The complete theoretical derivation innovatively applies MOS to sub-Nyquist sampling and presents a design method for MOS penalty constant. Extensive simulation results show that the proposed scheme can not only achieve blind sensing under the spectrum occupancy up to 40%, reduce the overall computational complexity of iterative SOMP, but also significantly improve the false alarm performance, meeting the requirements of the IEEE 802.22 standard.

Target tracking is an essential issue in wireless sensor networks (WSNs). Compared with single-target tracking, how to guarantee the performance of multi-target tracking is more challenging because the system needs to balance the tracking resource for each target according to different target properties and network status. However, the balance of tracking task allocation is rarely considered in those prior sensor-scheduling algorithms, which may result in the degradation of tracking accuracy for some targets and additional system energy consumption. To address this issue, we propose in this paper an improved Q-learning-based sensor-scheduling algorithm for multi-target tracking (MTT-SS). First, we devise an entropy weight method (EWM)-based strategy to evaluate the priority of targets being tracked according to target properties and network status. Moreover, we develop a Q-learning-based task allocation mechanism to obtain a balanced resource scheduling result in multi-target-tracking scenarios. Simulation results demonstrate that our proposed algorithm can obtain a significant enhancement in terms of tracking accuracy and energy efficiency compared with the existing sensor-scheduling algorithms.

Clustering for target tracking over wireless sensor networks (WSNs) is an important method to achieve a trade-off between network lifetime and tracking accuracy. Due to the stochastic and non-uniform distribution of nodes within the network, the untimely onset of coverage holes is exhibited in prevailing clustering schemes, thereby diminishing the overall network coverage performance. To address this issue, we propose the Nodal Importance-based Hierarchical Dynamic Clustering (NIHDC) scheme. Firstly, we introduce the concept of nodal importance rank to quantify the impact of regional states on individual nodes, which is correlated with the regional node density and energy levels. Secondly, by jointly considering energy factor and nodal importance rank, we propose an importance-ascending hierarchical scheduling algorithm to strike a balance between network lifetime and tracking accuracy, in which the Binary African Vulture Optimization Algorithm (BAVOA) is utilized to search for the optimal cluster. Simulation results demonstrate that, compared to existing protocols, NIHDC achieves at least 17.7% improvement in network lifetime while maintaining comparable tracking accuracy, and delays the emergence of coverage holes.

In large-scale wireless sensor networks (WSNs), spectrum efficiency and energy consumption are two challengeable problems to be solved due to the increasing traffic requirement and its limited energy. Successive interference cancellation (SIC) technology, which supports multiple parallel transmissions, gives an opportunity to improve the efficiency of data collection. However, those classical schemes suffer from high energy consumption, which presents a severe challenge to energy-constrained WSNs. To improve the transmission and energy efficiency of WSNs, we study an energy-efficient scheduling algorithm joint with SIC and power control in this paper. We first develop a non-uniform M-Level scheduling model to identify the beneficial opportunities and guarantee the transmission quality of SIC concurrency. Then, we formulate an energy efficiency maximization scheduling (EEMS) utility and further transform it into a combinatorial optimization problem. To reduce the computational complexity and obtain meaningful insights, two low-complexity optimization algorithms are proposed from the perspective of SIC scheduling and power control, respectively. Finally, extensive results demonstrate that the SIC scheduling design enables our proposed algorithm to achieve 51% higher throughput than the algorithm without SIC. Moreover, the energy efficiency of our proposed algorithm is improved by 37% compared with the existing algorithms, which indicates that our algorithm can search for the optimal configuration of SIC-concurrent transmissions and better manage the concurrent transmission power.