178 results about "Optimal deployment" patented technology

A system for computing an optimal deployment of at least one web application in a multi-datacenter system comprising a collector for collecting performance measurements with regard to a web application executed in the multi-datacenter system and grouping the performance measurements according to locations of a plurality of clients accessing the web application; a data repository for maintaining at least a performance table including at least the performance measurements grouped according to the plurality of client locations and a service level agreement (SLA) guaranteed to clients in the plurality of client locations; and an analyzer for processing at least information stored in the performance table for generating a recommendation on an optimal deployment of the web application in at least one combination of datacenters in the multi-datacenter system by computing an expected SLA that can be guaranteed to the clients in each combination of datacenters.

An integrated autonomic innovation infrastructure including an autonomic management system, a motivational signature management system and/or an innovation signature management system manages all aspects of innovation activity for an organization including maintaining a record of submissions of innovation and development thereof including support of collaboration, matching problems with solutions and review and evaluation thereof, management of employee motivation through matching of incentives to innovation and supporting optimal deployment of individuals within an organizational structure to support creative and innovative activity as well as handling consideration and collaboration in regard to submissions in regard to improvements in the infrastructure, itself. numerous feedback paths in each system and between systems allow adaptive optimization of each of the systems and the integrated infrastructure.

The coverage, connectivity and communication (C3) protocol method for wireless sensor networks is an integrated and energy-efficient protocol for Coverage, Connectivity and Communication (C3) in WSNs. The C3 protocol uses RSSI to divide the network into virtual rings, defines clusters with clusterheads more probably at alternating rings, defines dings, which are rings inside a cluster, uses triangular tessellation to identify redundant nodes, and communicates data to sink through clusterheads and gateways. The protocol strives for near-optimal deployment, load balancing, and energy-efficient communication. Simulation results show that the C3 protocol ensures partial coverage of more than 90% of the total deployment area, ensures 1-connected network, and facilitates energy-efficient communication, while expending only ¼th of the energy compared to other related protocols

The invention provides an unmanned aerial vehicle base station group deployment method based on time correlation. According to the method, a coverage blind area of a ground base station serves as a target area covered by an unmanned aerial vehicle base station group, and the deployment position and the deployment range of the unmanned aerial vehicle base station group are determined according to environmental parameters (position, size and building average height) of the target area. Because link condition fluctuation is caused by conversion between a sight distance link and a non-sight distance link in a three-dimensional space channel and change of shielding conditions of obstacles during transmission of an unmanned aerial vehicle base station, the instantaneous rate does not accuratelyrepresent the average rate of a network any more, time correlation is introduced, and network performance of large-scale time is obtained. Specifically, the interference correlation coefficient is used for measuring the time correlation, the joint coverage rate is used for representing the network performance of large-scale time, and the optimal deployment height and the number of unmanned aerialvehicle base stations are determined according to the change relation of the joint coverage rate along with the deployment height of the unmanned aerial vehicle base station group and the number of the unmanned aerial vehicle base stations in the group.

The invention discloses a searching deployment-control method for fake-licensed vehicles. The searching deployment-control method comprises the following steps of: 1) extracting data; 2) calculating the lower bound of a stroke time; 3) identifying the vehicles by a quick hash algorithm for license numbers; 4) calculating the suspicion degrees of the fake-licensed vehicles: when data with the same Hash code and the same license number, judging whether the vehicle is a fake-licensed suspected vehicle, and the steps are as follows: calculating a time suspicion degree, and calculating a summarized suspicion degree; 5) selecting the optimal deployment-control point: sequencing the fake-licensed suspicion degrees of the vehicles to obtain a suspected fake-licensed vehicle table, and when the suspicion degrees are greater than a set threshold value, then judging the vehicles to be the suspected fake-licensed vehicles; carrying out a track analysis on the suspected fake-licensed vehicles, establishing a vehicle running track table according to passage numbers and elapsed time, and taking the passage number with the highest times in the same time slot as the optimal deployment-control point. The invention discloses a searching deployment-control method for fake-licensed vehicles, which is fast in calculation speed, high in identification accuracy, good in reliability, and also with a deployment-control interception function.

A method for optimal deployment of reader antennas of an ultrahigh-frequency radio-frequency identification positioning system based on an improved chicken swarm optimization algorithm specifically comprises the following steps: determining the evaluation indexes of an optimization target for a deployment problem of multiple target reader positioning antennas; establishing an optimization objective function; determining the constraint conditions in the process of deployment optimization; designing an improved chicken swarm optimization algorithm; and carrying out iterative operation, outputting a global optimal fitness value and an optimal solution, getting a solution satisfying the need of ultrahigh-frequency radio-frequency identification positioning, and carrying out final scheduling on reader antennas. On the premise of meeting the reader antenna number and space constraint conditions, the geometric accuracy of positioning, the coverage of positioning and the communication interference are taken as optimization objectives, and the optimization problem is solved using the improved chicken swarm optimization algorithm. Therefore, the quality of the optimal solution is improved greatly while the convergence speed of the algorithm is ensured.

The invention relates to a multi-sink deployment and fault tolerance method for a wireless sensor network. A multi-sink optimal deployment method based on improved particle swarm optimization clustering is firstly brought forward, the improved particle swarm optimization is combined with K-means clustering, clustering and partitioning are carried out on all sensor nodes in a network area, the multiple sink nodes are deployed in the center of each cluster respectively, and thus, effects of reasonable partitioning and optimal multi-sink node deployment of the network are achieved. In addition, in view of a condition in which a certain sink node fails in the multi-sink network, a routing algorithm with a fault tolerance mechanism is brought forward, a fault tolerance mechanism with multi-path routing is adopted, two paths from a source node to different sink nodes are built, and when a certain sink node fails, alternate routing is activated. Through deploying multiple sink nodes in the wireless sensor network, the network reliability can be improved, when one sink node fails, other several sink nodes can replace the node for work, reliable operation of the network is ensured, and the service life of the network is prolonged.

The invention discloses a door-type RFID multi-antenna deployment system and a method based on intelligent prediction. The system comprises a door structure, a reader antenna, a reader, a tag to be identified, a database for storing RFID data, and a computer for controlling automatic RFID data acquisition and achieving model training. The method includes firstly performing modeling on an RFID system by using a support vector machine and a few training samples and through iterative on-line training so as to obtain an RFID system prediction model with the model generalization ability and the prediction accuracy satisfying user requirements; and selecting the optimal deployment position and direction of RFID multiple antennas by using the prediction model obtained through learning and through a genetic algorithm so as to obtain the best deployment effect of the multi-antenna RFID system in the shortest period. According to the system and the method, the model with high generalization and prediction abilities is obtained by using few RFID data training samples, therefore time and labor for mass acquisition of real RFID data are saved, and time for recognizing multi-antenna deployment positions and directions of tags to be recognized is obtained.

The invention discloses a wireless sensor network node optimal deployment method based on an improved wolf pack algorithm, which is applied to node optimal deployment of a wireless sensor network, improves the effective coverage rate of wireless sensor nodes, and uses a nonlinear convergence factor balance algorithm to perform earlier global search and later local search capability. An elitist strategy is added to accelerate the convergence speed of the algorithm. A dynamic weight strategy is provided, so that position updating of individuals with poor positions is more reasonable. Meanwhile,a dynamic position crossing processing strategy is provided, and the possibility of searching a global optimal solution in an area is increased. A dynamic variation strategy is introduced to increasethe diversity of wolf groups and effectively expand the search range of the algorithm. The method has the advantages that the problem that the GWO algorithm is prone to falling into local optimum later is solved. The IGWO algorithm improves the coverage performance of the nodes of the wireless sensor network, the higher coverage rate can be achieved with fewer nodes, coverage holes are reduced, and the deployment cost of the network is reduced.

The invention relates to a wireless sensor network deployment method comprising the following steps: acquiring communication radii and sensing radii of sensor nodes, wherein the communication radii of all sensor nodes in the wireless sensor network are same, and the sensing radii of all sensor node are same; acquiring the ratio of the communication radii to the sensing radii; finding the corresponding relation of a preset optimal deployment mode, determining the normalized deployment mode corresponding to the ratio of the communication radii to the sensing radii; and deploying the sensor nodes according to the determined normalized deployment mode. In the invention, according to the preset corresponding relation between rC/rS and different normalized optimal deployment modes, after the communication radii and the sensing radii are acquired, the optimal normalized deployment mode can be quickly determined by finding the corresponding relation, thereby reducing the networking cost of the wireless sensor network.

The invention discloses a greedy energy source minimization arrangement method of an RF (Radio Frequency)-energy harvesting wireless sensor network. From an initial state that the wireless sensor network is not provided with an RF-energy source, the following two parts of operation are repeatedly executed: increasing and deploying a new energy source, and improving the position of the energy source. The newly added energy source is put on a position which effectively improves node energy capture power. The operation of improving the energy source position is to iteratively improve the position of the traditional existing energy source to obtain the optimal deployment of the existing energy source. After the operation of improving the energy source position is finished, the energy capture power of each node is calculated, the phenomenon that the energy capture powers of all nodes meet demands means that the arrangement of the current energy source meets demands, and the arrangement method is finished. The greedy energy source minimization arrangement method of the RF-energy harvesting wireless sensor network can be effectively suitable for a situation that node energy capture power demands in a network are different, and is low in cost.

The invention relates to an energy source minimization arrangement method of a radio-frequency energy capturing wireless sensor network based on particle swarm optimization. From the beginning of an initial state that the wireless sensor network does not have a radio-frequency energy source, the following two steps are executed repetitively: a new radio-frequency energy sending source is additionally deployed; and improvement of K energy source positions based on particle swarm optimization is carried out; when a new energy source is additionally deployed every time, the initial position, the initial speed and the optimal position of each particle are necessary to initialize; then, the energy source position optimal operation based on particle swarm optimization is executed; and, for each K value, if the optimal deployment of K energy positions satisfies capturing power requirements of all sensor nodes after improvement of the energy source positions based on particle swarm optimization is executed, the energy source minimization arrangement method is ended. The energy source minimization arrangement method of the radio-frequency energy capturing wireless sensor network based on particle swarm optimization provided by the invention is capable of effectively reducing the arrangement number of energy sources and saving the economical cost.

Risk profiling in order to optimize the deployment of security measures such as behavior-blocking, hardening, or securing techniques is disclosed. Risk profiling includes evaluating a risk to a host service based on communication with a remote system, creating a risk profile for the host service, and deploying a security measure to protect the host service based on the risk profile. Risk profiling enables optimization of deployment of security measures to protect a host service that is either directly or indirectly communicating with a remote system. Using a risk profile enables the optimal deployment of security measures while preventing host system performance degradation and increased system requirements (e.g., increased memory and processor usage).

The invention discloses a bidirectional water flow-based multi-objective optimal deployment method of a water quality monitoring network. The method comprises: combining intersection points of water areas, location information of key points and flow directions of water flows into a directed-graph vertex set, and calculating distances between adjacent vertices to obtain a weighted directed graph by modeling. carrying out hydraulic modeling according to the weighted directed graph, simulating water quality pollution events at any time in a monitored area, and establishing hydraulic parameters of each monitored water area sections; determining an optimization object function; and carrying out multi-objective optimization according to a multi-objective particle swarm algorithm, and constantly adjusting particle moving speeds and positions to seek an optimal solution, wherein average detection times and successful-detection probabilities of pollution under the two different water flow directions are respectively calculated according to a cost function, then a total detection time and a total successful-detection probability of the pollution are calculated according to a time occupying ratio of the water flows of the two different water flow directions, and a Pareto front is searched to be an optimal solution set. According to the method, optimal deployment for a water quality monitoring system in the case of the bidirectional water flows can be realized, and optimization can be carried out according to different objectives.

The invention discloses a radiofrequency energy source optimal deployment method for supplying energy to a body area network node. The method comprises the following steps: modeling a mobility pattern of a user into a graph constructed by residence points and track edges; and iteratively calculating the position of a new energy source in stages according to different target functions, wherein charging power of the body area network node on all residence points is higher than energy consumption power under the action of the new energy source in a first stage, maximum energy accumulated net consumption values of all track edges do not exceed an energy storage element capacity of the node under the action of the new energy source in a second stage, and an actual energy non-interruption probability of the body area network node meets a system requirement under the action of the new energy source in a third stage. The method is suitable for scenes in which the body area network node can be charged through capture of radiofrequency energy; the energy source can be deployed reasonably according to the mobility pattern of the user; an energy non-interruption requirement of a system is met; and the deployment cost is lowered.

The invention relates to a relay node robustness covering method for a double-layer structure wireless sensor network. The method is a relay node 2-cover deployment algorithm based on local search, the global deployment problem is degraded to a local deployment problem, the robustness is guaranteed, and the optimal deployment is realized. The method specifically includes two steps: first 1-cover and secondary 1-cover, wherein the first 1-cover includes: three steps of relay node candidate deployment position construction, sensor node grouping, and relay node local deployment, sensors are grouped via a novel grouping method, the complexity of the algorithm is reduced, and the optimality of the deployment is guaranteed; and the secondary 1-cover includes: the threshold is adjusted, sensor nodes covered only by one relay node are selected from each group, 1-cover of the sensor nodes is performed by employing the 1-cover method, the robustness is guaranteed, the deployment quantity of the relay nodes is reduced, and the problem solving time is shortened.

The invention belongs to the field of cloud computing and multi-cloud service, and relates to a method for constructing a QoS-based application optimization deployment scheme in a multi-cloud environment. According to the method, an overall design framework is expanded, and the framework comprises the steps that for a user demand entity and a cloud service entity, corresponding entity models are designed to express configuration setting of two parties for application specifications, and the problems of SLA mechanism fuzziness and an objective function are solved. Aiming at an application optimization deployment problem in a multi-cloud environment, modeling is carried out based on a QoS model, and constraint conditions and an objective function of optimization deployment are provided. Meanwhile, aiming at single-cloud application deployment and cross-cloud application deployment requests, a multi-objective optimization strategy is applied, and different solutions are given by a heuristic search algorithm. By designing an effective application service optimal deployment scheme, the automation level and the service quality level of the cloud service are improved.

The present invention provides a method for optimal deployment of oilfield project groups based on 0-1 integer programming. The method for optimal deployment of oilfield project groups based on 0-1 integer programming includes: step 1, determining the decision variables of the optimization model; step 2, after deployment The maximum total net present value after conversion of all projects is the optimization goal, determine the objective function; Step 3, determine the exclusion constraints of capacity projects; Step 4, determine the decision variable constraints, to avoid the possibility of re-commissioning in subsequent years after the project is put into production; Step 5 , to determine the objective practical constraints and the subjective constraints of decision makers; step 6, use the ant colony algorithm to solve the optimization model, and obtain the optimal deployment of capacity construction projects. The optimal deployment method of oilfield project groups based on 0‑1 integer programming can meet the needs of optimal deployment of production capacity construction projects, improve the scientificity and rationality of deployment of oilfield production capacity investment projects, and provide strict and reliable Methods.

The invention provides a virtual network function deployment method suitable for multi-dimensional resource optimal configuration. The method comprises the following steps: step 1, acquiring a networkstate, and updating available resources of all nodes and residual bandwidths of links; 2, when a service function chain deployment request is detected, judging whether the network meets deployable conditions or not; 3, determining a deployable node set of the request according to the sequence constraint of each virtual network function in the request; 4, calculating the node resource capability and the path resource capability of each deployable node; 5, determining an optimal deployment path by adopting a service function chain deployment algorithm based on a Viterbi backtracking method; and6, deploying the virtual network functions according to the optimal deployment path and the node sequence to form a target service function chain, updating the network state, and returning to the step 1. According to the method, various resources can be balanced according to respective use degrees, and the network performance is considered while the overall resource consumption of network configuration is optimized.

The invention belongs to the technical field of radio-frequency communication, and relates to a reader optimization deployment method based on a passive ultrahigh-frequency RFID positioning system. The method comprises the steps: analyzing a relationship between a reader emission energy level and a radiation radius, constructing a target adaptive value function based on positioning time consumption according to the LANDMARC algorithm principle, taking positions of various readers in a system as optimization searching variables, taking the fact that the target adaptive value function is minimum as a optimization searching target, building an optimization searching particle model through adoption of the typical particle swarm algorithm, improving the local searching capability and the global searching capability through introducing the simulation annealing algorithm, and then determining an optimal deployment mode of the readers. The method is advantageous in that the LANDMARC algorithm principle and a passive RFID identification mechanism are integrated, the system time-consuming particle optimization searching model suitable for passive ultrahigh-frequency RFID positioning is built, and positioning efficiency of the system can be effectively improved through the obtained reader deployment mode.

The present invention puts forward an optimization deployment method for an image sensor network, comprising two basic steps, i.e., establishment of an observation model of an image sensor, and optimization deployment of an image sensor network. In particular, the optimization deployment method comprises: a first step of establishing the observation model of the image sensor based on characteristics of the image sensor observing an object; and a second step of establishing an optimization deployment function of the network by establishing an observation area model, a network coverage model, and an observation model of the network, and obtaining the optimal deployment of the network by optimizing the function. According to the optimization deployment method of the present invention, an observation performance function of the image sensor to the object is established, and the more perfect image sensor observation model is introduced into the optimization deployment of the image sensor network, thereby effectively solving the problems of low deployment and operation flexibility and low observation efficiency in the existing image sensor network.

The invention discloses a k-fence construction and charger optimal deployment method for a wireless rechargeable sensor network. The method comprises the following specific steps: step 1, constructing a fence diagram according to monitoring area information; and step 2, solving a fence network structure by using a minimum cost and maximum flow algorithm; step 3, finding each sensor node forming the fence according to the solving information of the minimum cost maximum flow algorithm; step 4, dividing the whole monitoring area into grid points, and calculating the effectiveness of arranging the charger at each grid point; and step 5, selecting a charger position according to the charging utility of each grid point; judging whether all sensor nodes meet the charging requirement or not, and if yes, ending; otherwise, returning to the step 4 to recalculate the charging effectiveness of the remaining grids. Fence coverage and charger deployment are combined, and the number of deployed chargers is reduced on the premise of ensuring the fence coverage requirement.

The invention provides an optimal deployment method of an unmanned aerial vehicle air base station in rapid recovery of railway emergency communication, and belongs to the field of railway communication engineering. The method comprises the following steps: firstly, constructing a position optimization problem of joint ground equipment/facility association and an unmanned aerial vehicle air base station in a two-dimensional space, wherein the target function is obtained by maximizing the total throughput of an air-ground link between the unmanned aerial vehicle and a user, and the constraint conditions are as follows: the throughput of the air-ground link between the unmanned aerial vehicle and the user is not less than a throughput threshold, and the position of the unmanned aerial vehicle is constrained in a fault area; decomposing the problem into two independent sub-problems, namely a user and unmanned aerial vehicle associated sub-problem and an unmanned aerial vehicle position optimization sub-problem; and optimizing and solving the two self-problems by utilizing an iterative algorithm to obtain a final unmanned aerial vehicle deployment position. According to the method, theoptimization position solution of the unmanned aerial vehicle is accelerated, and the emergency communication service capability of the unmanned aerial vehicle can be maximized on the premise that the communication requirements of ground communication equipment are met.

In a method for positioning an X-ray image acquisition device a straight reference plane (30) intersecting a three-dimensional representation of the object, a center point (34) within the intersection of the object, a normal vector (38) to the reference plane and at least one tangential vector (40) within the reference plane are created. Thereafter, the reference plane, the object's frame of reference and the X-ray image acquisition's frame of reference are registered. At least one viewing direction derived from the normal vector (38) and/or at least one tangential vector (40) is defined, wherein the X-ray image acquisition device is adjusted to the geometrical parameters of the X-ray image acquisition device. Thereby, planned and stored optimal viewing directions may be made available by a single push of a button, leading to automatically positioning of the X-ray image acquisition device and a much faster adjustment of the live guidance image, thus resulting in less exposure of radiation as well as a less cumbersome adjustment procedure. Furthermore a more optimal deployment of the interventional devices can be reached, since they can be more accurately positioned.