Energy Constraints Of Sensors Research Articles

Joint Optimization of UAV Trajectory and Sensor Uploading Powers for UAV-Assisted Data Collection in Wireless Sensor Networks

In this article, we investigate the energy minimization problem of an unmanned-aerial-vehicle (UAV)-assisted data collection sensor network. We jointly optimize the trajectory of the UAV and the power consumption of the sensors for data uploading with the power and energy constraints of sensors. The trajectory design consists of two parts: 1) the serving orders for sensors and 2) the UAV’s hovering positions, where the latter is highly coupled with the power consumption of the sensors. To find the optimal serving orders of sensors, we formulate the problem as a standard traveling salesman problem (TSP), which can be optimally solved by the efficient Cutting-Plane method. To solve the UAV position and sensor uploading power optimization problem, we propose the PSPSCA algorithm that optimizes the transmit power by the pattern search method, while the UAV’s hovering positions are optimized by the successive-convex-approximation (SCA) method in the inner loop. To deal with the high computational complexity of the PSPSCA algorithm, we analyze the analytical relationship between optimal sensor uploading power and the UAV’s hovering positions, based on which we simplify the optimization problem and propose the AQSCA algorithm as an alternative approach. Simulation results have validated that the proposed algorithm outperforms the existing benchmark schemes.

Finite-time synchronisation for periodic delayed master-slave neural networks with weighted try-once-discard protocol

This paper focuses on the finite-time (FT) synchronisation for master-slave periodic neural networks (NNs) with mixed time-varying delays. In order to make full use of the limited network bandwidth caused by sensor energy constraint, a transmission channel assignment method is established, and a weighted try-once-discard (WTOD) protocol is employed for each channel. A WTOD protocol-dependent periodic controller is designed to ensure that the slave periodic NNs can synchronise with the master one. Based on a periodic Lyapunov–Krasovskii function, sufficient conditions are established to guarantee that the synchronisation error system is FT bounded. Finally, a numerical example is presented to demonstrate the validity of the theoretical results.

Ensembled Elbow and Bray-Curtis Fuzzy C-Means Clustering For Energy Efficient Data Aggregation in WSN

Wireless sensor network (WSN) comprises the distributed sensors for aggregating and organizing the data. Data aggregation is the major concern in WSN since it relies on several factors, namely energy constraints of sensors, network topology, links conditions and so on. The conventional approach does not perform efficient data aggregation due to their battery power of nodes and degrade the network lifetime. To improve data aggregation and network lifetime, An Energy-Efficient Ensembled Elbow Fuzzy C-means Clustering based Data Aggregation (EEEEFCC-DA) method is designed. Initially, residual energy of each sensor node (SN) is calculated. To determine the number of clusters, the elbow method is used in fuzzy c-means clustering algorithm. Then, Centroids value is calculated for every cluster to group SNs. Bray-Curtis Similarity Index is used to compute the similarity between the SN and Centroids value of cluster. SNs are grouped depends on the similarity value. The process gets iterated until every SNs gets clustered to the suitable clusters. After that, the SN with higher residual energy is selected as cluster head (CH). CH gathers data from each SNs and send to sink node. This, assist to enhance the data gathering accuracy and lessen the energy consumption. Simulation of EEEEFCC-DA method is carried out with various metrics namely energy consumption, network lifetime, data aggregation accuracy (DAA) and data aggregation time with number of SNs and number of data packets (DP). Results show that EEEEFCC-DA method provides better performance in term of DAA , network lifetime , energy consumption and data aggregation time than the conventional methods.

Toward Energy-Oriented Optimization for Green Communication in Sensor Enabled IoT Environments

One of the major bottlenecks toward realizing IoT systems is the energy constraint of sensors. Prolonging network lifetime is a fundamental issue for implementing IoT systems. The energy optimization problem, being NP-hard in nature for scalable networks, has been addressed in the literature using traditional metaheuristic techniques. Quantum inspired metaheuristics have shown better performance than their traditional counterparts in solving such optimization problems in different domains. Toward this end, this article proposes a quantum inspired green communication framework for Energy Balancing in sensor enabled IoT systems (Q-EBIoT). First, an energy optimization model for sensor enabled IoT environments is presented, where energy consumption is derived as cost of the energy-oriented paths. Second, a quantum computing oriented solution is developed for the optimization problem focusing on energy centric solution representation, measurement, and rotation angle. The proposed solution is implemented to evaluate the comparative performance with the state-of-the-art techniques. The evaluation demonstrates the benefit of the proposed framework in terms of various energy-related metrics for sensor enabled IoT environments.

Optimal periodic scheduling for remote state estimation under sensor energy constraint

The authors consider a sensor scheduling problem for a scalar system observed by two sensors. The measurements derived from the two local sensors are sent over a bandwidth-limited network to a remote estimator. The bandwidth constraint allows at most one packet can be sent at each time step. Upon receiving the data, the estimator computes the optimal estimate of the state in the minimum mean-squared error sense. In consideration of simplicity as well as practical and efficient implementation, the authors focus on periodic scheduling scheme. The authors assume that the sensor energy is so limited that in each period there exist open-loop predictions at some steps, which makes the problem more challenging. Using some novel tools, it was shown that the `as uniformly as possible' scheme, originally proposed in an early work for minimising the average estimation error covariance when sensor energy is just enough, still holds true in this limited energy case but the optimal steps of open-loop prediction depend on the system parameters. Such a relationship between scheduling rule and system parameters is novel and does not exist in early works where sensor energy is just enough. Numerical examples are provided to demonstrate the key ideas of the proposed work.

Real-time query processing optimization for cloud-based wireless body area networks

Wireless body area networks (WBANs) have received a lot of attention from both academia and industry due to the increasing need of ubiquitous computing for eHealth applications, the continuous advances in miniaturization of electronic devices, and the ultra-low-power wireless technologies. In these networks, various sensors are attached either on clothes, on human body or even implanted under the skin for real-time health monitoring of patients in order to improve their independent daily lives. The energy constraints of sensors, the vital and large amount of data collected by WBAN nodes require powerful and secure storage, and a query processing mechanism that takes into account both real-time and energy constraints. This paper addresses these challenges and proposes a new architecture that combines a cloud-based WBANs with statistical modeling techniques in order to provide a secure storage infrastructure and optimize the real-time user query processing in terms of energy minimization and query latency. Such statistical model provides good approximate answers to queries with a given probabilistic confidence. Furthermore, the combination of the model with the cloud-based WBAN allows performing a query processing algorithm that uses the error tolerance and the probabilistic confidence interval as query execution criterions. The performance analysis and the experiments based on both real and synthetic data sets demonstrate that the new architecture and its underlying proposed algorithm optimize the real-time query processing to achieve minimal energy consumption and query latency, and provide secure and powerful storage infrastructure.