Polling Points Research Articles

MANAGEMENT CONTROL IN THE PROCESS OF IMPLEMENTATION OF INNOVATION PROJECTS

The scientific publication is dedicated to highlighting the systemic vision of the basics of management controlling in the process of implementing innovative projects and revealing the features of this type of controlling. It has been established that controlling the process of implementation of innovative projects is a special type of controlling, associated with their individuality and the absence of analogs and established patterns of analysis and control, and with the uncertainty of the final results of the project, its individual stages. It is substantiated that the control process is a process of step-by-step correction of expected project results, gradual control of parameters as the final result is approached. The procedure for determining the coefficient of change in the speed of work, which characterizes the execution of the network or operational-calendar schedule at the time of control, is considered. It is proposed to determine the relationship between the informational importance of control points and the implementation coefficient based on the value of the financial “penalty” function. The matrix of information weights of control points is considered, which is the basis for determining the sequence of polling points (objects) of control. The procedure for determining the step (control lag) for two interconnected innovative projects during the technological rehabilitation of production is revealed. The distribution function or the probability of the absence of uncertainty in the process of implementing works on the implementation of an innovative technological project before the moment of control is determined. Options for choosing the frequency of control point polls for operational management are substantiated. It was established that from the point of view of the stability of the information system, the algorithm for determining the maximum frequency of checks on the status of project implementation work should take into account the possibility of falling behind the given schedules and reducing the amount of remaining work, which results in an increase in the number of control point surveys. The limit of the increase is the time required for the implementation of decisions on operational regulation of the speed of work execution by production divisions (executors).

Mobile Collector-Based Cost Balancing Scheme for Uniform Data Gathering Delay and Energy Consumption in Wireless Sensor Actuator Networking Systems

The sink hole problem is a critical issue for data collection in wireless sensor networks. Recent research has indicated that the mobility of actuators in wireless sensor actuator networks (WSANs) alleviates the sink hole problem. However, mobile actuator-based data collection may result in unsatisfactory data gathering delay, which is not acceptable for delay-sensitive real time applications. Thus, planning the optimal path for mobile collector is a critical challenge in WSANs to balance between the prolongation of network lifetime and the reduction of the data gathering delay. To address this issue, we propose a mobile collector-based cost balancing algorithm to achieve uniform data gathering delay and energy consumption based on the adaptive region partitioning and dynamic polling point selection. The proposed algorithm uses multiple mobile collectors and partitions the sensing area into several regions that are monitored by the mobile collectors according to the distribution of cost in the area. It also dynamically determines moving trajectory of each mobile collector by selecting the polling points to visit considering the distance to travel and the nodes’ energy consumption. The superiority of the proposed algorithm is validated by theoretical analysis and simulation results.

An Efficient Data Gathering Technique Using Optimal Minimum Coverage Spanning Tree Algorithm in WSN

A WSN system incorporates a gateway that provides wireless connectivity back to the wired world and distributed nodes. Many existing schemes were utilized for the purpose of increasing the data gathering capacity of sensor nodes from source to sink node. But the existing schemes suffered from maximum distance problem of reaching the sink node. Hence, to overcome those issues, we propose an energy efficient data gathering technique in WSN. Initially, in this paper, the sensor nodes are clustered using LEACH protocol. After the clustering process, cluster heads (CHs) are selected. These selected CHs in all the clusters are used for transferring the messages to the mobile node for data gathering. Then, optimal data transmission path is selected with the help of Optimal Minimum Covering Spanning Tree (OMST) algorithm. The OMST algorithm enhances data gathering and reduces the duration for reaching the sink node via selection of polling points. For reducing time complexity in MST, an efficient optimization algorithm is used. For an optimization purpose, particle gene swarm (PGS) model is used. From the experimental results, the proposed method achieved better results than the existing method in terms of energy, overhead, delay, delivery ratio, NLT and throughput.

Allocating Travel Times Recorded from Sparse GPS Probe Vehicles into Individual Road Segments

Abstract Probe-based data collection system, which deploys movable sensors in traffic streams to collect traffic records, is commonly used for area-wide measurement of travel times and conditions of traffics on road networks. However, due to sparse distribution of polling positions from a low probe sampling frequency in practice, the sampled points from probe vehicles do not necessarily match with the end points of each link, and the travel time between two consecutive polling points needs to be positioned into an individual link for link travel time calculation. This paper presents the new technique that addresses the allocation of travel times based on data gathered from sparse GPS probe vehicles. The travel time allocation procedure by applying the relationship among instantaneous speeds, sampled positions and tracked times is proposed. The proposed technique is discussed and compared with a ground truth benchmark of high resolution field data recorded from urban roadway corridor in both congested and uncongested traffic conditions. Results suggest that addressing travel time allocation problems by the proposed method significantly outperforms the benchmark method in both complete link and local levels which are demonstrated by an increasing estimation accuracy up to 60%. It is also observed from the local level analysis that the accuracy figures are lower at the intersections comprising high stopped delays, which normally contain highly fluctuated speed profiles. Nonetheless, the proposed technique outperforms the baseline approach in both intersections with low or high stopped delays. The proposed technique offers superior results to the baseline technique in all scenarios, particularly on the segments with highly fluctuated speed profiles.

Mobile Data Gathering with Load Balanced Clustering and Dual Data Uploading in Wireless Sensor Networks

In this paper, a three-layer framework is proposed for mobile data collection in wireless sensor networks, which includes the sensor layer, cluster head layer, and mobile collector (called SenCar) layer. The framework employs distributed load balanced clustering and dual data uploading, which is referred to as LBC-DDU. The objective is to achieve good scalability, long network lifetime and low data collection latency. At the sensor layer, a distributed load balanced clustering (LBC) algorithm is proposed for sensors to self-organize themselves into clusters. In contrast to existing clustering methods, our scheme generates multiple cluster heads in each cluster to balance the work load and facilitate dual data uploading. At the cluster head layer, the inter-cluster transmission range is carefully chosen to guarantee the connectivity among the clusters. Multiple cluster heads within a cluster cooperate with each other to perform energy-saving inter-cluster communications. Through inter-cluster transmissions, cluster head information is forwarded to SenCar for its moving trajectory planning. At the mobile collector layer, SenCar is equipped with two antennas, which enables two cluster heads to simultaneously upload data to SenCar in each time by utilizing multi-user multiple-input and multiple-output (MU-MIMO) technique. The trajectory planning for SenCar is optimized to fully utilize dual data uploading capability by properly selecting polling points in each cluster. By visiting each selected polling point, SenCar can efficiently gather data from cluster heads and transport the data to the static data sink. Extensive simulations are conducted to evaluate the effectiveness of the proposed LBC-DDU scheme. The results show that when each cluster has at most two cluster heads, LBC-DDU achieves over 50 percent energy saving per node and 60 percent energy saving on cluster heads comparing with data collection through multi-hop relay to the static data sink, and 20 percent shorter data collection time compared to traditional mobile data gathering.

English

In large-scale wireless sensor networks the data- gathering mechanism to introducing mobility into the network. . We consider the location service in a WSN, where each sensor needs to maintain its location information by 1) frequently updating its location information within its neighboring set of polling points in the network. In the previous work a mobile data collector, for convenience called an M-collector for gathering information from the sensor and send to sink node. M- collector could be a mobile robot or a vehicle equipped with a powerful transceiver and battery, working like a mobile base station and gathering data while moving through the field. An M-collector starts the data-gathering tour periodically from the static data sink, polls each sensor while traversing its transmission range, then directly collects data from the sensor in single-hop communications, and finally transports the data to the static sink. Since data packets are directly gathered without relays and collisions, the lifetime of sensors is expected to be prolonged. In this paper, we mainly focus on the problem of minimizing the length of each data-gathering tour and remove the redundancy for data gathering. We develop a stochastic sequential decision framework to analyze this problem. Under a Markovian mobility model, the location update decision problem is modeled as a Markov Decision Process (MDP). For the work with strict distance/ time and energy constraints, we consider utilizing single and multiple M-collectors and propose a data- gathering algorithm where multiple M-collectors traverse through several shorter subtours concurrently to satisfy the distance/time and energy constraints. To identify the redundancy of data from the sensor using Pearson auto correlated technique. Our proposed system for mobile data gathering scheme can improve the scalability and balance the energy consumption among sensors.

Lifetime enhancement in wireless sensor and actuator network using uniform energy consumption algorithm

This paper studies the moving path planning problem of mobile actuator in delay-constrained wireless sensor and actuator networks. We consider a real-time scenario for mission-critical applications, where the data gathering must be performed within a specified latency constraint. To maximise the network lifetime subject to the delay constraint, we construct an energy-efficient shortest path tree in which the sensors’ residual energy is taken into consideration. Then, based on the shortest path tree, we present a dynamic cost algorithm in which the sensor with more energy has higher possibility to be selected as polling point. Finally, the moving path of mobile actuator is determined by solving the travelling salesman problem including all the polling points. The effectiveness of our algorithm is validated by extensive simulations.

A Uniform Energy Consumption Algorithm for Wireless Sensor and Actuator Networks Based on Dynamic Polling Point Selection

Recent research has indicated that using the mobility of the actuator in wireless sensor and actuator networks (WSANs) to achieve mobile data collection can greatly increase the sensor network lifetime. However, mobile data collection may result in unacceptable collection delays in the network if the path of the actuator is too long. Because real-time network applications require meeting data collection delay constraints, planning the path of the actuator is a very important issue to balance the prolongation of the network lifetime and the reduction of the data collection delay. In this paper, a multi-hop routing mobile data collection algorithm is proposed based on dynamic polling point selection with delay constraints to address this issue. The algorithm can actively update the selection of the actuator's polling points according to the sensor nodes' residual energies and their locations while also considering the collection delay constraint. It also dynamically constructs the multi-hop routing trees rooted by these polling points to balance the sensor node energy consumption and the extension of the network lifetime. The effectiveness of the algorithm is validated by simulation.

Bounded Relay Hop Mobile Data Gathering in Wireless Sensor Networks

Recent study reveals that great benefit can be achieved for data gathering in wireless sensor networks by employing mobile collectors that gather data via short-range communications. To pursue maximum energy saving at sensor nodes, intuitively, a mobile collector should traverse the transmission range of each sensor in the field such that each data packet can be directly transmitted to the mobile collector without any relay. However, this approach may lead to significantly increased data gathering latency due to the low moving velocity of the mobile collector. Fortunately, it is observed that data gathering latency can be effectively shortened by performing proper local aggregation via multihop transmissions and then uploading the aggregated data to the mobile collector. In such a scheme, the number of local transmission hops should not be arbitrarily large as it may increase the energy consumption on packet relays, which would adversely affect the overall efficiency of mobile data gathering. Based on these observations, in this paper, we study the tradeoff between energy saving and data gathering latency in mobile data gathering by exploring a balance between the relay hop count of local data aggregation and the moving tour length of the mobile collector. We first propose a polling-based mobile gathering approach and formulate it into an optimization problem, named bounded relay hop mobile data gathering (BRH-MDG). Specifically, a subset of sensors will be selected as polling points that buffer locally aggregated data and upload the data to the mobile collector when it arrives. In the meanwhile, when sensors are affiliated with these polling points, it is guaranteed that any packet relay is bounded within a given number of hops. We then give two efficient algorithms for selecting polling points among sensors. The effectiveness of our approach is validated through extensive simulations.