Pranali D. Tembhurne

Bio: Pranali D. Tembhurne is an academic researcher. The author has contributed to research in topics: Power optimization & Mobile station. The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

Power saving strategies in wireless sensor networks

Abstract: IEEE 802.16e is an emerging standard for mobile wireless broadband access systems. In any mobile networks, power saving is one of the most important features for the extension of deviceslifetime. IEEE 802.16e is the latest broadband wireless access standard designed to support mobility. To enhance the power efficiency of broadband wireless sensor networks based on battery power, power saving strategies based on cross-layer design is proposed for IEEE 802.16e sleep-mode operation. By analyzing the behaviors of a mobile station working in the sleep-mode operation with a Markov chain model and exploring the relationship among traffic, energy consumption and mean delay of power saving mechanism based on cross-layer design, a heuristic algorithm is proposed to adapt the parameters of sleep-mode mechanism to the mobile station traffic and the mean delay requirement.

A review of stochastic battery models and health management

Abstract: Batteries are promising sources of green and sustainable energy that have been widely used in various applications Battery modelling as the basis of battery management system is vital for both technology development and applications of batteries Compared with other battery models, stochastic battery models feature high accuracy and low time consumption Moreover, charging profile, battery behavior, and discharging profile can all be considered to optimize battery performance and usage, which is a key issue in battery usage in real life Given the significance of stochastic modelling and the progress of battery health management, this paper reviews various aspects of related studies and developments from different fields, while identifying their corresponding merits and weaknesses Remaining challenges are discussed, and several suggestions are offered as possible inspirations for further research

Power saving for real-time services in multiuser OFDMA-based cognitive radio systems under average interference constraint

Abstract: This paper considers a power minimization problem for Quality-of-Service provisioning in the downlink of multiuser OFDMA-based cognitive radio systems with real-time services by jointly optimizing over subcarrier and power allocation. An average interference constraint is imposed to protect the primary transmission. The optimal solution is obtained by using Lagrange methods. It can be seen that the optimal power allocation follows a modified water-filling approach with different water levels for different subcarriers and users. An optimal algorithm and a suboptimal algorithm based on stochastic nature are then proposed. Numerical comparisons show that the performance of the suboptimal algorithm with low complexity is close to that of the optimal one, which demonstrate that the suboptimal algorithm outperforms other algorithms.

Power saving for cooperative spectrum sharing-based cognitive radios under primary user short-term rate protection

Abstract: In cooperative spectrum sharing-based cognitive radio systems, the secondary user (SU) acts as a cooperator of the primary user (PU) to assist the PU's transmission and is allowed to simultaneously transmit at the same spectrum only if it is not harmful to the primary transmission. Here the authors studies a power minimising problem of the SU with real-time services for this system under constraints on the SU's quality-of-service requirement and guaranteeing the PU's instantaneous rate unchanged if the SU allocates part of its power for its own transmissions. Superposition coding (SC) is further employed by the SU to eliminate the interference caused by the PU. The problem is formulated as an optimisation problem. Owing to its high complexity, this problem is then converted into a single variable optimisation problem, and a greedy-based power minimisation method with low complexity is thus proposed. Numerical comparison shows that the proposed method can lead to reductions in the secondary transmission power of about 30 and 14% against for the non-SC one and the equal bit allocation-based one, respectively.

EAPESS: An Adaptive Transmission Scheme in Wireless Sensor Networks

Abstract: 4 Abstract: Reduced energy consumption in sensor nodes is one of the major challenges in Wireless Sensor Networks (WSNs) deployment. In this regard, Error Control Coding (ECC) is one of techniques used for energy optimization in WSNs. Similarly, critical distance is another term being used for energy efficiency, when used with ECC provides better results of energy saving. In this paper three different critical distance values are used against different coding gains for sake of energy saving. If distance lies below critical distance values then particular encoders are selected with respect to their particular coding gains. Coding gains are used for critical distances estimation of all encoders. This adaptive encoder and transmit power selection scheme with respect to their coding gain results in a significant energy saving in WSNs environment. Simulations provide better results of energy saving achieved by using this adaptive scheme.

Random Forest Based Li-Ion Battery Capacity Prediction Subject to Capacity Recovery Effect

Abstract: In decades, a series of researches on capacity prediction have been conducted where capacity degradation processes have been assumed to be irreversible. This paper presents an approach considering the capacity recovery effect taken place during dynamic fading processes. In this approach, firstly, some features significantly affecting the level of battery capacity recovery are comprehensively analyzed and extracted. Then the random forest (RF) is applied to model and cognize the capacity degradation process with consideration of the capacity recovery effect. To verify the efficiency of the proposed approach, Experiments based on published datasets have been conducted by comparison. The results point out that the proposed method could cognize the real maximum capacity more accurately with the consideration of capacity recovery effect.