Minimum Response Time Research Articles

Minimizing Response Time with Optimal Fire Station Allocation

Quick response time in emergency situation is critical to protect human lives. In the fast-growing cities, fire departments can fall behind the standard response time due to cities’ expansion. This research focuses on ways to improve the response time of a city’s emergency situation. A Non-linear Programming model is used to determine the locations of fire stations, so that they can cover the maximum number of residents, in terms of the geographical area and population. The model is applied to the city of Kingsville, Texas to check the practicability. The results of the research indicate that optimized locations make population coverage increment up to 15% and geographic coverage increment up to 21% with two fire stations. With three fire stations including a newly added fire station, the population coverage goes up to 48% increment and the geographic coverage increased up to 71%, which covers 88% of total city population.

DA-DRLS: Drift adaptive deep reinforcement learning based scheduling for IoT resource management

In order to fulfill the tremendous resource demand by diverse IoT applications, the large-scale resource-constrained IoT ecosystem requires a robust resource management technique. An optimal resource provisioning in IoT ecosystem deals with an efficient request-resource mapping which is difficult to achieve due to the heterogeneity and dynamicity of IoT resources and IoT requests. In this paper, we investigate the scheduling and resource allocation problem for dynamic user requests with varying resource requirements. Specifically, we formulate the complete problem as an optimization problem and try to generate an optimal policy with the objectives to minimize the overall energy consumption and to achieve a long-term user satisfaction through minimum response time. We introduce the paradigm of a deep reinforcement learning (DRL) mechanism to escalate the resource management efficiency in IoT ecosystem. To maximize the numerical performance of the entire resource management activities, our method learns to select the optimal resource allocation policy among a number of possible solutions. Moreover, the proposed approach can efficiently handle a sudden hike or fall in users' demand, which we call demand drift, through adaptive learning maintaining the optimal resource utilization. Finally, our simulation analysis illustrates the effectiveness of the proposed mechanism as it achieves substantial improvements in various factors, like reducing energy consumption and response time by at least 36.7% and 59.7% respectively and increasing average resource utilization by at least 10.4%. Our approach also attains a good convergence and a trade-off between the monitoring metrics.

Placement of Data Array Replicas in a Distributed System With Unreliable Communication Channels

Abstract The paper proposes an approach to solving the problem of optimal placement of data array replicas in a distributed system. It presents a formal model of a distributed system with unreliable communication channels. A system is represented as a graph with coloured arcs. The formulation of the problem of finding the optimal placement of replicas is given. As a criterion for optimization, the minimum response time to a data request is considered. The task solution heuristic algorithm is also proposed in the paper.

Strategic Positioning and Competitive Advantage of Commercial Banks in Uasin Gishu County, Kenya

Strategic positioning has been identified as an important weapon to manage the competition in the business environment. However, despite the use of strategic positions, many banks in the country are facing challenges from the competition destabilizing the position they hold. Thus, the sole reason of the project was to establish how strategic positioning affects competitive advantage of banks in Uasin Gishu County Kenya. The specific objectives were; to examine the effects of mono-segment positioning, multi-segment positioning, standby positioning, adaptive positioning and defensive positioning on competitive advantage of commercial banks in Uasin Gishu County, Kenya. The study was guided by Resource-Based-View, The Game Theory, Market Based View Theory and Open Systems Theory. Explanatory research design was used in this study. The unit of analysis was commercial banks in Uasin Gishu County, Kenya and the unit of observation was 748 employees in 25 banks. A stratified random sample was used as a significant blend of categorization and randomization. A sample of 173 employees was selected. Questionnaires were used in collection of data. Descriptive statistics and multiple regression analysis were used to analyse data. The study established a positive and significant relationship between mono-segment positioning, multi-segment positioning, standby positioning, adaptive positioning and defensive positioning on organizational competitive advantage. The study concludes that through mono-segment Commercial banks are free to devote all of their resources to attracting a single, narrowly defined type of customer with a specific need it can fulfill better than other companies. Through multi segment Commercial banks can analyze various customer groups and targeting specific products to meet their demands. Through standby positioning, commercial banks can base their strategy completely on a new segment which increases its focus and profitability. Through adaptive the brand loyalty of commercial banks definitely increases and also market segmentation increases competitiveness of a firm from a holistic view and defensive positioning leads to customer retention throughout customer life cycle. The study recommends that commercial banks in Uasin Gishu County implementing mono segment should concentrate all their efforts in a single segment with a single marketing mix so as to avoid confrontation with financial institutions. On multi segment, commercial banks should categorize their customers along demographic, geographic, behavioral, or psychographic lines or a combination of them. This will enable them offer products and services effectively by understanding distinctive needs of the groups. On standby positioning strategy, commercial banks should only opt execute a mono-segment positioning strategy only during unavoidable situations. To minimize response time, the banks should prepares a standby plan that specify the product(s) and their attributes as well as details of the marketing program(s) that would be used to position the new product. On adaptive positioning, commercial banks should aim at changing or reforming a bank’s marketing mix to suit to the particular geography in which the bank is operating. This will enable the banks to effectively tailor their products and service in rapid and unparalleled ways to meet their customers’ interests and needs. On defensive positioning, commercial banks should resort to position defense to ensure a new bank’s market entry does not impact or weaken their brand. They may also opt to preempt competitive strategies by introducing an additional brand in a similar position for the same segment.
 This is an open-access article published and distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License of United States unless otherwise stated. Access, citation and distribution of this article is allowed with full recognition of the authors and the source.

Overview of the Development of the Maritime Search and Rescue System in Croatia

According to the Convention for the Safety of Life at Sea and International Convention on Maritime Search and Rescue, saving human lives at sea is the duty of all signatory states. This paper analyzes and gives an overview of previous research activities in search and rescue system at sea and how the use of unmanned aerial vehicles (UAV) can improve search and rescue actions at sea. Research activities include development of the search system and placement of resources that are used in search and rescue actions (ships, planes etc.). Previous research is mainly related to minimizing response time when accidents at sea are detected in relation to search and rescue missions. Implementation of unmanned aerial vehicles into the search and rescue system enables improvement of these actions due to earlier detection and verification of accidents at sea and prevents unnecessary search and rescue units engagement in cases when an accident did not occur. The results of previous research point to the fact that future research should aim to explore the synthesis of unmanned aerial vehicles with the existing search and rescue system at sea in Croatia.

Priority-Based and Optimized Data Center Selection in Cloud Computing

Cloud computing (CC) is rapidly emerging in IT fields and becomes a vital research area. CC environment allows cloud service providers to offer different types of services to customers of different categories. Customers like their requests to be processed at the lowest cost and minimum response time, which is highly dependent on the receiving datacenter (DC). Selecting a DC is entitled to the service broker that operates in accordance with a built-in routing policy. Improper selection of a DC may cause a bottleneck in the service brokerage architecture, resulting in resource bleeding (time and money). Moreover, cloud services are charged based on a pay-per-use model. Therefore, this paper proposes an efficient service broker routing policy that improves users’ satisfaction and cloud performance. The proposed policy employs the Vector Space Model and a multi-objective scalarization function to optimize conflicting objectives. The simulation-based approach was used to test the performance of the proposed methodology. The results show the ability of the proposed methodology to improve DC selection process based on the user’s priorities. Moreover, the performance of the proposed methodology has been compared with related works; the results show a significant improvement in average DC processing time, response time and total cost.

A framework based on location hazard index for optimizing operational performance of emergency response strategies: The case of petrochemical industrial cities

Emergency response operations are very important activities in the industrial areas. As an incident in an industrial area can have considerable economic and social impact, response to such an incident has to be provided in a very short time. Decisions for such responses should consider the available emergency resources, and other factors such as humidity, wind speed, wind direction, population, chemicals, and materials handling, and the characteristics of surrounding affected industrial facilities. In this paper, an index-based emergency response management system (IERMS) is proposed by integrating the location hazard index (LHI) and the response time optimization model. The LHI prioritizes the hazard potential of a location. The response time optimization model provides minimum response time and resource allocations in response to an emergency situation. The use of the framework is illustrated in an industrial city case study by considering petrochemical fire as a demonstration example. The results obtained from the analysis show that the framework is applicable to design the response to incident conditions. It is expected that the framework would help in better resource allocation and efficient response to incidents in industrial areas.

Real Time Multi Object Detection for Blind Using Single Shot Multibox Detector

According to world health statistics 285 million out of 7.6 billion population suffers visual impairment; hence 4 out of 100 people are blind. Absence of vision restricts the mobility of a person to pronounced extent and hence there is a need to build an explicit device to conquer guiding aid to the prospect. This paper proposes to build a prototype that performs real time object detection using image segmentation and deep neural network. Further the object, its position with respect to the person and accuracy of detection is prompted through speech stimulus to the blind person. The accuracy of detection is also prompted to the device holder. This work uses a combination of single-shot multibox detection framework with mobileNet architecture to build rapid real time multi object detection for a compact, portable and minimal response time device construction.

A geometric framework for modeling dynamic decisions among arbitrarily many alternatives

Multiple-choice and continuous-response tasks pose a number of challenges for models of the decision process, from empirical challenges like context effects to computational demands imposed by choice sets with a large number of outcomes. This paper develops a general framework for constructing models of the cognitive processes underlying both inferential and preferential choice among any arbitrarily large number of alternatives. This geometric approach represents the alternatives in a choice set along with a decision maker’s beliefs or preferences in a “decision space,” simultaneously capturing the support for different alternatives along with the similarity relations between the alternatives in the choice set. Support for the alternatives (represented as vectors) shifts over time according to the dynamics of the belief / preference state (represented as a point) until a stopping rule is met (state crosses a hyperplane) and the corresponding selection is made. This paper presents stopping rules that guarantee optimality in multi-alternative inferential choice, minimizing response time for a desired level of accuracy, as well as methods for constructing the decision space, which can result in context effects when applied to preferential choice.

Fog-Based Multi-Class Dispatching and Charging for Autonomous Electric Mobility On-Demand

Despite the significant advances in vehicle automation and electrification, the next-decade aspirations for massive deployments of autonomous electric mobility on demand (AEMoD) services in big cities are still threatened by two major bottlenecks, namely, the communication/computation and charging delays. In order to target the communication/computation delays, the paper suggests the exploitation of fog-based architectures for localized AEMoD system operations. These emerging architectures are soon to become widely used, allowing for all localized operational decisions to be made with very low latency by fog controllers located close to the end applications (e.g., each city zone for AEMoD systems). As for the charging delays, an optimized multi-class charging and dispatching queuing model, with partial charging option for AEMoD vehicles is developed for each of these zones. The stability conditions of this model and the optimal number of classes are then derived. The decisions on the proportions of each class vehicles to partially/fully charge or directly serve customers are optimized to minimize the maximum and average system response times using convex optimization and Lagrangian analysis. The results show the merits of our proposed model and optimized decision scheme compared to both the always-charge and the equal-split scheme. Furthermore, the comparison of the maximum and average response time minimization results shows a very low variance in performance, which suggests by using the linear programming solution for lower complexity.

Development of A Fingerprint Biometric Authentication System For Secure Electronic Voting Machines

Democratic government in the world today rely on electronic voting as the foremost means of providing credible, transparent and fair elections for the electorate. There is a need for developed electronic voting systems to be security enhanced to ensure the authenticity of the developed system. Traditional paper balloting systems suffer from vote tampering, multiple voting and illegal voting by unregistered voters. They are also, susceptible to under aged voting due to the difficulty in authenticating the identity of prospective voters. Manual collation and publication of vote results also leads to slow response times and inaccuracies in published results. This research paper proposes a system to combat the current challenges through the development of a fingerprint biometric authentication system for secure electronic voting machines. It uses a fingerprint biometric sensor, integrated via Python to verify users of the system. The inclusion of biometrics improves the security features of the system. The secure voting system is built using PHP and easy to use Graphical User Interface was designed using HTML and CSS. Users are required to interact with the machine via a 7” touchscreen interface. From the results, it shows that the developed machine has a minimum response time of 0.6 seconds for specific operation, an FAR of 2%, FRR of 10% and overall system accuracy of 94%. The developed machine is able to combat the challenges of authentication of users, thereby guaranteeing the transparency, credibility, integrity and vote authenticity of the elections.

Optimal Edge Resource Allocation in IoT-Based Smart Cities

With IoT-based smart cities, massive heterogeneous IoT devices are running diverse advanced services for unprecedented intelligence and efficiency in various domains of city life. Given the exponentially growing number of IoT devices and the large number of smart city services as well as their different QoS requirements, it has been a big challenge for servers to optimally allocate limited resources to all hosted applications for satisfactory performance. Note that by pushing the computing and storage resources to the proximity of end IoT devices, and deploying applications in distributed edge servers, edge computing technology appears to be a promising solution for this challenge. Toward this, we study in this article how to allocate edge resources for average service response time minimization. Besides the proposed algorithms, extensive numerical results are also presented to validate their efficacy.

A Hybrid Approach for Providing Improved Link Connectivity in SDN

Software-Defined Networking (SDN) is a unique approach to design and build networks. The networks services can be better handled by administrators with the abstraction that SDN provides. The problem of re-routing the packets with minimum overhead in case of link failure is handled in this work. Protection and restoration schemes have been used in the past to handle such issues by giving more priority to minimal response time or controller overhead based on the use case. A hybrid scheme has been proposed with per-link Bidirectional forwarding mechanism to handle the failover. The proposed method makes sure that the controller overhead does not impact the flow of packets, thereby decreasing the overall response time, even with guaranteed network resiliency. The computation of the next shortest backup path also guarantees that the subsequent routing of packets always chooses the shortest path available. The proposed method is compared with the traditional approaches and proven by results to perform better with minimal response time.

Discriminating between anticipatory and visually triggered saccades: measuring minimal visual saccadic response time using luminance.

We describe a novel behavioral method to accurately discriminate anticipatory (i.e., saccades not generated by visual input) from visually triggered saccades and to identify the minimal visual saccadic reaction time (SRT). This method can be used to calculate a feasible lower bound cutoff for latencies of visually triggered saccades within a certain experimental context or participant group. We apply this method to compute the minimal visual SRT for two different saccade target luminance levels. Three main findings are presented: 1) the minimal visual SRT for all participants was 46 ms shorter for bright targets than for dim targets, 2) the transition from non-visually triggered to visually triggered saccades occurred abruptly, independent of target luminance, and 3) although the absolute minimal visual SRTs varied between participants, the response pattern (response to bright targets being faster than to dim targets) was consistent across participants. These results are consistent with variability in saccadic and neural responses to luminance as has been reported in monkeys. On the basis of these results, we argue that differences in the minimal visual SRT can easily occur when stimuli vary in luminance or other saliency features. Applying an absolute cutoff (i.e., 70-90 ms) that approaches the minimal neuronal conduction delays, which is general practice in many laboratories, may result in the wrongful inclusion of saccades that are not visually triggered. It is suggested to assess the lower SRT bound for visually triggered saccades when piloting an experimental setup and before including saccades based on particular latency criteria. NEW & NOTEWORTHY We successfully developed an anticipation paradigm to discriminate between anticipatory and visually triggered saccades by measuring the minimal visual saccadic response time (SRT). We show that the 70- to 90-ms lower bound cutoff for visually triggered saccades should be applied in a flexible way and that the transitional interval is very short. The paradigm can be employed to investigate the effects of different stimulus features, experimental conditions, and participant groups on the minimal visual SRT in humans.

A New Hybrid Genetic Algorithm-based Approach for Critical Multiprocessor Real-Time Scheduling with Low Power Optimization

This paper work deals with the presentation of an hybrid genetic approach which allocates and schedules, on a multiprocessor architecture, a system of real-time tasks while balancing the load on the processors. In addition, this hybrid heuristic approach takes into account the safety critical applications which is the focus of our work. We have carried out a performance analysis showing that the proposed hybrid genetic approach has results better than the classical GAs in terms of load balancing, minimum response time and good flexibility.

Enhanced Search Engine Using Proposed Framework and Ranking Algorithm Based on Semantic Relations

Today, most users need search engines to facilitate search and information retrieval processes. Unfortunately, traditional search engines have a significant challenge that they should retrieve high-precision results for a specific unclear query at a minimum response time. Also, a traditional search engine cannot expand a small, ambiguous query based on the meaning of each keyword and their semantic relationship. Therefore, this paper proposes a comprehensive search engine framework that combines the benefits of both a keyword-based and a semantic ontology-based search engine. The main contributions of this work are developing an algorithm for ranking results based on fuzzy membership value and a mathematical model of exploring a semantic relationship between different keywords. In the conducting experiments, eight different test cases were implemented to evaluate the proposed system. Executed test cases have achieved a precision rate of 97% with appropriate response time compared to the relevant systems.

Pilot Placement Schemes for Channel Estimation of Proposed 5G-GFDM System

Orthogonal Frequency Division Multiplexing (OFDM) is a highly regarded technique used in the 4G mobile communication systems to provide reliable communication and high data rates due to the orthogonality between its sub carriers. However, it cannot be used in the next generation cellular system i.e. 5G. Thus, a new technique Generalized Frequency Division Multiplexing (GFDM) has been proposed to meet the demands of the next generation systems, which are higher data rates than 4G, minimum response time, lower power consumption etc. GFDM is a non-orthogonal, multicarrier scheme, which seems to fulfil the requirements of the new wireless communication system. The aim of this paper is to use the pilot symbols and their optimum placements within the data for the channel estimation of the GFDM system. It is shown that the optimum arrangement of the pilot symbols is to place them uniformly on equal intervals within the data and to cluster them in the middle of the data.

Performance Analysis of a Hybrid Approach to Enhance Load Balancing in a Heterogeneous Cloud Environment

Cloud computing is rapidly becoming more advanced. It has become more attractive due to its cloud potentials of being easy to use and anywhere accessibility in comparison to other technologies. Load balancing is a very important component of efficient operations in the cloud computing environment. Several algorithms have been designed but yet unable to rectify the holes found in the performance area like minimizing response time, processing time and cost. In this work, it is proposed a hybrid approach that is devoted to enhancing performance for the cloud system user putting the concepts of enhanced Throttled and Equally spread current execution algorithms(ESCE) into use. The proposed hybrid algorithm was implemented in cloud analyst environment and the results obtained were analyzed. The performance of the proposed hybrid algorithm was evaluated with the existing algorithms ESCE and the improved throttled using the response time, data processing time, cost, physical memory and storage capacity. The results of the evaluation analysis show that with network delay, physical memory and the storage capacity, the Performance differences in various scenario vary, where scenario 1 is 10.5 (ms) average response time and processing time, $ 2.10 total cost. Scenario 2 ,5.6 (ms) response time and 8.5(ms)processing time $2.03 cost but the ESCE recoded best minimum response time of 40.30(ms). Scenario3,8.3 (ms) response time, 3.5 (ms)processing time,$2.00 cost. scenario 4 of 0.31(ms) response time, the processing time of 2.41(ms) and cost of $2.14 but the improved throttled is best in terms of minimum processing time with 2.00(ms).scenario 5,2.3 (ms) response time ,processing time 1.1 (ms)and cost of $2.00. Considering the physical memory, the fixed physical memory provided a better average response time of 0.50(ms) than the varied physical memory, and the varied physical memory provides a better maximum processing time of 5.03(ms). In terms of storage capacity, high storage capacity provides a better response time of 320.02 (ms). This work recommends the adaptation of hybridized improved Throttled and ESCE algorithms by cloud service providers so as to get better performances.

Solving Problem of Optimal Placement Information Resources on the Nodes of a Distributed Information Processing System with a Self-Similar Input Flow of Applications

This article presents the results of numerical experiments to solve the problem of optimal placement of information resources on the nodes of a distributed information processing system (DIPS) by the criterion of the minimum average response time of the system to user requests, implemented on the basis of the architecture "file-server", two-level and three-level architecture "client-server" with self-similar input traffic applications. Approximate methods of stochastic Queuing networks research (decomposition approximation method and mean value analysis method) and simulation method were used as modeling methods. The obtained results of numerical experiments are compared with the results of solving the same problems using the exact method of analysis of exponential Queuing networks. By results of the comparative analysis of the received results conclusions are drawn.

Performance Related Tradeoffs between Fairness and Throughput for Job Scheduling in Cloud Environment

The burst of data volume and application complexity, it has become prevalent to host large-scale computations in clusters of distributed servers. In shared production clusters, job scheduling is of paramount importance to the cluster performance. The two basic scheduling objectives are efficiency and fairness---an ideal scheduler shall facilitate fast job response, and meanwhile avoid starvation by guaranteeing worst-case service quality to each job.For inter-job scheduling, efficiency and fairness are conflicting with each other, leading to a dilemma of either predictable performance at the expense of long response time, or minimum mean response time at the risk of starvation. As a result, it’s critical to develop resource scheduling strategies that can do well in both worlds. In this regard, we make the following contributions.The study of existing scheduling approaches is carried out and two new approaches have been proposed. In the first approach- ‘Novel Hybrid Cost based Priority (NHCP)’, the tasks are assigned profit threshold and priorities. In the second approach- ‘Two Party Model (TPM)’, that allows lower priority tasks to be migrated and allocated from slow to fast resource even during low priority if fast resource is free i.e. higher priority resource. In both the approaches we have maximized throughput, minimized makespan and also tried to reduce average waiting time. It is observed that proposed approaches perform better than the existing approaches under different work load conditions.