Optimal Placement Strategy Research Articles

A Heuristic Approach for Optical Transceiver Placement to Optimize SNR and Illuminance Uniformities of an Optical Body Area Network

The bi-directional information transfer in optical body area networks (OBANs) is crucial at all the three tiers of communication, i.e., intra-, inter-, and beyond-BAN communication, which correspond to tier-I, tier-II, and tier-III, respectively. However, the provision of uninterrupted uplink (UL) and downlink (DL) connections at tier II (inter-BAN) are extremely critical, since these links serve as a bridge between tier-I (intra-BAN) and tier-III (beyond-BAN) communication. Any negligence at this level could be life-threatening; therefore, enabling quality-of-service (QoS) remains a fundamental design issue at tier-II. Consequently, to provide QoS, a key parameter is to ensure link reliability and communication quality by maintaining a nearly uniform signal-to-noise ratio () within the coverage area. Several studies have reported the effects of transceiver related parameters on OBAN link performance, nevertheless the implications of changing transmitter locations on the uniformity and communication quality have not been addressed. In this work, we undertake a DL scenario and analyze how the placement of light-emitting diode (LED) lamps can improve the uniformity, regardless of the receiver position. Subsequently, we show that using the principle of reciprocity (POR) and with transmitter-receiver positions switched, the analysis is also applicable to UL, provided that the optical channel remains linear. Moreover, we propose a generalized optimal placement scheme along with a heuristic design formula to achieve uniform and illuminance for DL using a fixed number of transmitters and compare it with an existing technique. The study reveals that the proposed placement technique reduces the fluctuations in by and improves the illuminance uniformity up to as compared to the traditional approach. Finally, we show that, for very low luminous intensity, the values remain sufficient to maintain a minimum bit error rate (BER) of with on-off keying non-return-to-zero (OOK-NRZ) modulation format.

Advanced optimal sensor placement for Kalman-based multiple-input estimation

The direct measurement of the external loads acting on a mechanical component represents often a challenge in many engineering applications. In this context, the attention of many researchers is focused on the field of inverse load identification. Several techniques are proposed in literature to address this problem by means of experimental methodologies, often coupled with simulation solutions. In this paper, a Kalman-based methodology is considered, which solves the problem of inverse load identification in a predictive manner. The common issue of most of the techniques is the selection of an optimal set of sensors which gives the best load estimation. In the Kalman-filtering framework, an Optimal Sensor Placement (OSP) strategy has been proposed by the authors and it aims to find the best set of sensors in terms of system observability, which is the minimum requirement of a stable estimation. However, this does not guarantee the most accurate load estimation. In this contribution, two alternative metrics are proposed, based on: i) steady-state error covariance of the estimation, ii) estimator bandwidth, with respect to the available set of measurements. Both criteria will be included in the existing OSP to improve the sensors selection. A comparison of the three strategies for multiple input/state estimation is discussed on an industrial-scale Finite Element Model, in order to show the improvement in the accuracy of the estimated quantities.

Assessment of Ammunition Group Explosiveness in Destructive Effects Conditions and Ways to Reduce It

The ammunition placement in a small area creates the group explosion danger as a result of destructive effects. It is impossible to ensure the group explosive safety of ammunition, by increased distance between them, due to the limited volume in which they are placed. This can be achieved by using protective screens against the high-speed strikers impact. Another direction is the search for optimal ammunition placement schemes in storage that minimize the number of fragments falling into ammunition. The neutrons external background influence during the group storage of nuclear ammunition is also considered.

Cyber attacks on PMU placement in a smart grid: Characterization and optimization

Phasor measurement units (PMUs) have been integrated into the smart grid for monitoring the operational state of system and improving the reliability. Due to the high cost of PMU installment, the optimal placement strategies have attracted considerable attention in the literature. However, the impacts of cyber threats on the placement have been largely ignored owing to the cyber complexities. This paper initializes the study on the optimal PMU placement in a smart grid under the cyber threats. A probabilistic model is developed for assessing the unobservable risk of the power grid. We characterize the impacts of several cyber factors on the PMU placements including the number of directly attacked PMUs, the dependence among attack outcomes, and risk propagation. We further study the impacts of cyber attacks on the allocation strategies under a bi-level placement model. In particular, a novel ‘greedy’ algorithm for PMU placement is introduced with the presence of cyber risks. Our studies show that the cyber risk can significantly increase the unobservability risk of a power system which in turn requires additional PMU allocations, and the dependence among cyber attacks can lead to more unobservable risk.

A multi‐objective genetic algorithm strategy for robust optimal sensor placement

AbstractThe performance of a monitoring system for civil buildings and infrastructures or mechanical systems depends mainly on the position of the deployed sensors. At the current state, this arrangement is chosen through optimal sensor placement (OSP) techniques that consider only the initial conditions of the structure. The effects of the potential damage are usually completely neglected during its design. Consequently, this sensor pattern is not guaranteed to remain optimal during the whole lifetime of the structure, especially for complex masonry buildings in high seismic hazard zones. In this paper, a novel approach based on multi‐objective optimization (MO) and genetic algorithms (GAs) is proposed for a damage scenario driven OSP strategy. The aim is to improve the robustness of the sensor configuration for damage detection after a potentially catastrophic event. The performance of this strategy is tested on the case study of the bell tower of the Santa Maria and San Giovenale Cathedral in Fossano (Italy) and compared to other classic OSP strategies and a standard GA approach with a single objective function.

Robust Placement of Water Quality Sensor for Long-Distance Water Transfer Projects Based on Multi-Objective Optimization and Uncertainty Analysis

It is important to place water quality sensors along open channels in long-distance water transfer projects optimally for rapid source identification and efficient management of sudden water contamination. A new framework which considers multiple objectives, including earliest detection time, lowest missing detection rate and lowest sensor cost, and combines the randomness of injected contaminant type and contaminant incident consisting of contaminant intrusion location, time and mass, was established to obtain optimal placement of water quality sensor with better robustness in this paper. The middle route of the South-to-North Water Diversion Project in China was chosen as a case study, and it was found that both missing detection rate and detection time decrease with sensor cost gradually; furthermore, given the higher detecting precision, the detection accuracy and efficiency would be improved, a smaller number of water quality sensors would be needed, and the ten key placement positions where sensor with different detecting precision placed could be identified. Under the constraints of the allowable maximum missing detection rate, 1.00%, and detection time, 120.00 min, the detecting precision of 0.20 mg/L and 38 sensors placed could be selected as the optimal sensor placement scheme. Finally, with the consideration of contaminant uncertainty, the sensor placement scheme with better robustness could be constructed. The proposed framework would be helpful in solving the problem of water quality sensor placement with high practicality and efficiency in long-distance water transfer projects.

Deep neural network-based strategy for optimal sensor placement in data assimilation of turbulent flow

This paper focuses on the optimal sensor placement (OSP) strategy based on a deep neural network (DNN) for turbulent flow recovery within the data assimilation framework of the ensemble Kalman filter (EnKF). The assimilated flow field can be obtained using EnKF by optimizing the Reynolds-averaged Navier–Stokes (RANS) model constants. A feature importance layer was designed and used in a DNN to obtain the spatial sensitivity with respect to the RANS model constants. Two flow configurations experimentally measured using particle image velocimetry—i.e., a free round jet flow at Rej=6000 and a separated and reattached flow around a blunt plate at Reb=15 800—were selected as the benchmarks to demonstrate the effectiveness and robustness of the proposed strategy. The results indicated that the RANS models with EnKF augmentation were substantially improved over their original counterparts. A comprehensive investigation demonstrated that the selection of the five most sensitive sensors by DNN-based OSP can efficiently reduce the number of sensors and achieve a similar or better-assimilated performance over that obtained using all data in the entire flow field as observations.

Multiple Solutions for Optimal PMU Placement Using a Topology-Based Method

This paper presents a novel topology-based optimal PMU placement strategy. The proposed strategy provides the entire feasible solution space which gives the observability of the power system by working on binary connectivity matrix of the system. From the feasible solution space, multiple optimal solutions are obtained. Providing multiple optimal solutions gives flexibility to the system operator to choose one optimal solution which best fits sub-ordinate objectives rather than providing single optimal solution which gives no choice to the operator. This novel strategy has been tested on IEEE 14-bus and IEEE 30-bus system. Multiple optimal solutions are obtained while maintaining observability and maximizing redundancy. For demonstrating the advantage of multiple optimal solutions, minor objectives, such as direct monitoring of generator and weak buses are illustrated. One of the multiple solution which best fits to the minor objectives is chosen as a final optimal solution. The proposed method ensures global optimal solution for various objectives under consideration. In addition, normalized Bus observability index and normalized System observability redundancy index are presented to overcome the drawback of SORI and BOI.

Anatomical study of the ideal cortical bone trajectory in the lumbar spine.

Background:To explore the ideal trajectory of lumbar cortical bone trajectory screws and provide the optimal placement scheme in clinical applications.Methods:Lumbar computed tomography (CT) data of 40 patients in our hospital were selected, and the cortical vertebral bone contour model was reconstructed in three dimensions (3D). Depending on the different regions of the screw through the entrance and exit of the pedicle, 9 trajectories were obtained through combinational design: T-Aa, T-Ab, T-Ac, T-Ba, T-Bb, T-Bc, T-Ca, T-Cb, and T-Cc. Cortical bone trajectory (CBT) screws with appropriate diameters were selected to simulate screw placement and measure the parameters corresponding to each trajectory (screw path diameter, screw trajectory length, cephalad angle, and lateral angle), and then determine the optimal screw according to the screw parameters and screw safety. Then, 23 patients in our hospital were selected, and the navigation template was designed based on the ideal trajectory before operation, CBT screws were placed during the operation to further verify the safety and feasibility of the ideal trajectory.Results:T-Bc and T-Bb are the ideal screw trajectories for L1–L2 and L3–L5, respectively. The screw placement point is located at the intersection of the inner 1/3 vertical line of the superior facet joint and the bottom 1/3 horizontal line of the outer crest of the vertebral lamina (i.e., 2–4 mm inward at the bottom 1/3 of the outer crest of the vertebral lamina). CBT screws were successfully placed based on the ideal screw trajectory in clinical practice. During the operation or the follow-up period, there were no adverse events.Conclusion:CBT screw placement based on the ideal screw trajectory is a safe and reliable method for achieving effective fixation and satisfactory postoperative effects.

Function Approximation Based Reinforcement Learning for Edge Caching in Massive MIMO Networks

Caching popular contents in advance is an important technique to achieve low latency and reduced backhaul congestion in future wireless communication systems. In this article, a multi-cell massive multi-input-multi-output system is considered, where locations of base stations are distributed as a Poisson point process. Assuming probabilistic caching, average success probability (ASP) of the system is derived for a known content popularity (CP) profile, which in practice is time-varying and unknown in advance. Further, modeling CP variations across time as a Markov process, reinforcement Q-learning is employed to learn the optimal content placement strategy to optimize the long-term-discounted ASP and average cache refresh rate. In the Q-learning, the number of Q-updates are large and proportional to the number of states and actions. To reduce the space complexity and update requirements towards scalable Q-learning, two novel (linear and non-linear) function approximations-based Q-learning approaches are proposed, where only a constant (4 and 3 respectively) number of variables need updation, irrespective of the number of states and actions. Convergence of these approximation-based approaches are analyzed. Simulations verify that these approaches converge and successfully learn the similar best content placement, which shows the successful applicability and scalability of the proposed approximated Q-learning schemes.

Cluster load based content distribution and speculative execution for geographically distributed cloud environment

The scale of big data has shown an explosive growth, which makes the processing of big data put forward higher requirements on data centers, and a single data center can no longer meet the needs of big data processing. To deal with this situation, a geographically distributed cloud system needs to be built. However, in the geographically distributed cloud system, each data center is distributed in different geographic locations, which makes the data placement operations in the geographically distributed cloud system lead to greater overhead. To solve this problem, this paper proposes a data placement strategy. This strategy comprehensively considers the data transmission latency, bandwidth cost, cloud server storage capacity, and load capacity during the data placement process, and formulates a data placement problem that minimizes the energy consumption of data transmission. Then the minimum set cover method based on Lagrangian relaxation is used to solve this problem and obtain the optimal data placement scheme. On the other hand, in a geographically distributed cloud data center, the execution progress of the job submitted by the user will be affected by the straggler task. To solve this problem, this paper proposes a speculative execution strategy for the geographically distributed cloud system. This strategy performs different speculative execution operations according to the state of the cluster load, and then calculates the load capacity of the nodes in the cluster. The node with the strongest load capacity in the cluster is used to perform speculative execution operations. Experimental results show that the proposed data placement strategy can effectively improve the performance of the energy consumption, the data storage cost, the network transmission cost and the data transmission time. The proposed speculative execution strategy can effectively improve the performance of the job completion time, cluster throughput and QoS satisfaction rate.

A novel genetic algorithm with CDF5/3 filter-based lifting scheme for optimal sensor placement

A novel genetic algorithm with CDF5/3 filter-based lifting scheme for optimal sensor placement

A novel genetic algorithm with CDF5/3 filter-based lifting scheme for optimal sensor placement

The generic algorithm has been receiving significant attention due to the node placement problem in the field of sensor application in terms of machine learning. Sensor deployment is able to provide maximum coverage and maximum connectivity with less energy consumption to sustain the network lifetime. The maximum quality coverage problem has been solved successfully by an evolutionary algorithm while placing nodes in optimal position. In evolutionary algorithms, genetic algorithm (GA) plays an important technique for deploying the sensor in the form of population matrix. However, the existing techniques are unable to place sensor position perfectly. In this paper, a novel genetic algorithm with second generation wavelet transform (SGWT) is proposed for identifying optimal potential position for node placement. In order to improve the quality of population matrix, bi-orthogonal Cohen-Daubechies-Feauveau wavelet (CDF 5/3) has been employed. The proposed method is performed primarily to generate sensor position with different populations. Subsequently, it can extend to CDF5/3 filter-based lifting scheme to adjust the sensor position. The proposed method has been compared with random deployment, genetic algorithm and GA with CDF5/3 wavelets in terms of target to cover by the sensor. The result of the proposed method affirms better optimisation as compared to the state-of-art techniques.

Deep reinforcement learning for multi-objective placement of virtual machines in cloud datacenters

The ubiquitous diffusion of cloud computing requires suitable management policies to face the workload while guaranteeing quality constraints and mitigating costs. The typical trade-off is between the used power and the adherence to a service-level metric subscribed by customers. To this aim, a possible idea is to use an optimization-based placement mechanism to select the servers where to deploy virtual machines. Unfortunately, high packing factors could lead to performance and security issues, e.g., virtual machines can compete for hardware resources or collude to leak data. Therefore, we introduce a multi-objective approach to compute optimal placement strategies considering different goals, such as the impact of hardware outages, the power required by the datacenter, and the performance perceived by users. Placement strategies are found by using a deep reinforcement learning framework to select the best placement heuristic for each virtual machine composing the workload. Results indicate that our method outperforms bin packing heuristics widely used in the literature when considering either synthetic or real workloads.

Optimal Placement Strategy for Indoor Environment Monitoring using Portable Cost-Effective Devices

Optimal Placement Strategy for Indoor Environment Monitoring using Portable Cost-Effective Devices

Optimal placement strategy for indoor environment monitoring using portable cost-effective devices

Optimal placement strategy for indoor environment monitoring using portable cost-effective devices

Optimal Sensor Placement for Target Localization Using Hybrid RSS, AOA and TOA Measurements

This letter investigates the optimal sensor placement strategy for single static target localization using the hybrid received-signal-strength (RSS), angle-of-arrival (AOA) and time-of-arrival (TOA) measurements on the 2-dimensional (2D) plane. Firstly, the problem formulation is introduced. Secondly, the theoretical smallest trace of the Cramer-Rao lower bound (CRLB), which is equivalent to the smallest estimation mean-squared-error (MSE), is derived using the A-optimality criterion. Besides, we propose that the reachable smallest trace of the CRLB (tr(CRLB)) will be determined, once the number and characters of the sensors are selected. Thirdly, the corresponding optimal sensor placement strategies are proposed and concluded in two observations. Finally, the findings are demonstrated by simulation examples.

Optimal Reference Selection for Random Access in Predictive Coding Schemes

Data acquired over long periods of time like High Definition (HD) videos or records from a sensor over long time intervals, have to be efficiently compressed, to reduce their size. The compression has also to allow efficient access to random parts of the data upon request from the users. Efficient compression is usually achieved with prediction between data points at successive time instants. However, this creates dependencies between the compressed representations, which is contrary to the idea of random access. Prediction methods rely in particular on reference data points, used to predict other data points. The placement of these references balances compression efficiency and random access. Existing solutions to position the references use ad hoc methods. In this paper, we study this joint problem of compression efficiency and random access. We introduce the storage cost as a measure of the compression efficiency and the transmission cost for the random access ability. We express the reference placement problem that trades storage with transmission cost as an integer linear programming problem. Considering additional assumptions on the sources and coding methods reduces the complexity of the search space of the optimization problem. Moreover, we show that the classical periodic placement of the references is optimal, when the encoding costs of each data point are equal and when requests of successive data points are made. In this particular case, a closed-form expression of the optimal period is derived. Finally, the proposed optimal placement strategy is compared with an ad hoc method, where the references correspond to sources where the prediction does not help reducing significantly the encoding cost. The proposed optimal algorithm shows a bit saving of −20% with respect to the ad hoc method.

An optimal sensor placement strategy for reliable expansion of mode shapes under measurement noise and modelling error

Modal expansion techniques are typically used to expand the experimental modal displacements at sensor positions to all unmeasured degrees of freedom. Since in most cases, sensors can be attached only at limited locations in a structure, an expansion is essential to determine mode shapes, strains, stresses, etc. throughout the structure which can be used for structural health monitoring. Conventional sensor placement algorithms are mostly aimed to make the modal displacements at sensor positions of different modes as linearly independent as possible. However, under the presence of modelling errors and measurement noise, an optimal location based on this criterion is not guaranteed to provide an expanded mode shape which is close to the real mode shape. In this work, the expected value of normal distance between the real mode shape and the expanded mode shape is used as a measure of closeness between the two entities. Optimal sensor locations can be determined by minimizing this distance. This new criterion is applied on a simple cantilever beam and an industrial milling tower. In both cases, by using an exhaustive search of all possible sensor configurations it was possible to find sensor locations which resulted in a significant reduction in the distance when compared to a conventional optimal sensor placement strategy. Sufficiently accurate sub-optimal sequential sensor placement algorithm is also suggested as an alternative to the exhaustive search which is then compared with a genetic algorithm-based search. The efficiency of this new sensor placement criterion is further verified using Monte Carlo simulations for some realistic modelling error conditions.

Linear Wireless Sensor Networks Energy Minimization Using Optimal Placement Strategies of Nodes

Nowadays, the recent developments in the field of wireless sensor networks (WSNs) have initiated new applications of WSNs which can be used in many fields, such as military, environment, health, home and industry. One of the emerged wireless sensor networks topologies are linear wireless sensor networks (LWSNs). They have been rising as a great focus area of research. Such wireless sensor networks have a large number of applications such as border monitoring, railway track monitoring, structural health monitoring of bridges, health care and machines surveillance. LWSNs are widely applied in oil and gas pipelines infrastructure monitoring applications to enable the automatic measurement, analyses, storage and transmission of real-time data. Minimization of energy consumption of LWSNs is crucial for their proper usage. Using two different system models, this research investigates the minimization of LWSNs energy consumption using optimal node placement strategies compared to simple equal-distance placement scheme.