Reinforcement Learning (RL) has emerged as a promising approach for improving the performance of Wireless Sensor Networks (WSNs). The Q-learning technique is one approach of RL in which the algorithm continuously learns by interacting with the environment, gathering information to take certain actions. It maximizes performance by determining the optimal result from that environment. In this paper, we propose a data gathering algorithm based on a Q-learning approach named Bounded Hop Count -Reinforcement Learning Algorithm (BHC-RLA). The proposed algorithm uses a reward function to select a set of Cluster Heads (CHs) to balance between the energy-saving and data-gathering latency of a mobile Base Station (BS). In particular, the proposed algorithm selects groups of CHs to receive sensing data of cluster nodes within a bounded hop count and forward the data to the mobile BS when it arrives. In addition, the CHs are selected to minimize the BS tour length. Extensive experiments by simulation were conducted to evaluate the performance of the proposed algorithm against another traditional heuristic algorithm. We demonstrate that the proposed algorithm outperforms the existing work in the mean of the length of a mobile BS tour and a network's lifetime.INDEX TERMS Wireless sensor networks, mobile data gathering, delay tolerance, relay hop count, mobile base station tour.
In this paper, a permutation-based chaos system, named as Single Reference Permutation Index with Dual Modulation Differential Chaos Shift Keying (SR-PIDM-DCSK), is developed and tested. The proposed system uses the chaotic segment and its reversed version to modulate two pairs of data sets simultaneously. It uses the same reference for multiple symbol modulation. This significantly reduces bit energy requirement and enhances the Bit Error Rate (BER). In addition, it reduces the complexity of the system. At the transmitter, the reference signal is sent first, then the same reference is delayed and permutated to send the first information set of bits, while the same version is time reversed and permutated to modulate the second set of bits. Both segments are added together on the same symbol duration slot for transmission. This process is repeated for multiple symbols in a frame. At the receiver, the incoming reference signal is delayed for several symbol durations for demodulation. The BER of the system is evaluated in various channel environments. Moreover, a theoretical prediction for BER formula is developed for the suggested model. Results show that the proposed system has superior BER performance compared with other standard chaos based systems by an average of 2 dB. It is evident that the BER performance is enhanced with the increase in the spreading factor and the number of symbols in a frame. The theoretical formula for BER prediction is validated by computer simulation. Excellent matching was found between the theoretical formula and simulation results.
In this paper, a new scheme based on permutation index-differential chaos shift keying is proposed, modeled, and evaluated in AWGN channel environment. Data is sent by frames, each frame is headed by a single reference signal and followed by some information bearing signals. Modulation is performed through permutations of a reference signal according to the mapped data. At the receiver, each incoming information bearing signal undergoes all inverse permutation possibilities to perform a correlation with the delayed and stored version of the received reference signal. To decode the information bits, the detector selects the highest correlator outputs.
The proposed scheme named Single Reference-Permutation Index-Differential Chaos Shift Keying (SR-PI-DCSK) is an enhanced version of PI-DCSK, and uses a single reference signal for multiple information bearing ones. Hence, the energy requirement is saved by almost a half. The bit error performance is studied using the baseband system model and analytically tested using Gaussian Approximation (GA) method. Results show the BER performance outperforms other standard and recently developed differentially coherent chaos systems, including Permutation Index-DCSK by an average of $2.25$~db. Moreover, the analytical form which is developed to predict the Bit Error Rate (BER) is validated by simulation. Results demonstrate the performance in AWGN is closely matching with the simulation results, particularly at high SNR.
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