A Strategic Framework for Searching Mobile Targets Using Mobile Sensors

Hazra, Tanmoy; Nene, Manisha J.; Kumar, Suthikshn

doi:10.1007/s11277-017-4113-7

Cited by 7 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This developed model also generates results which are more reliable than comparable models, due to its capability of random walk in transition matrix and memoryless property. This viewpoint is also consisted with the earlier findings of Piccardi et al [36], Hazra et al [37], and Tserenjigmid [38]. Collectively, this study outlines a critical role in developing a model that provides a comprehensive yet simple prediction, which can help solve complicated forecasting problems.…”

Section: Discussionsupporting

confidence: 72%

Markov Chain Model Development for Forecasting Air Pollution Index of Miri, Sarawak

et al. 2019

View full text Add to dashboard Cite

A Markov chain is commonly used in stock market analysis, manpower planning, and in many other areas because of its efficiency in predicting long run behavior. However, the Air Quality Index (AQI) suffers from not using a Markov chain in its forecasting approach. Therefore, this paper proposes a simple forecasting tool to predict the future air quality with a Markov chain model. The proposed method introduces the Markov chain as an operator to evaluate the distribution of the pollution level in the long term. Initial state vector and state transition probability were used in forecasting the behavior of Air Pollution Index (API) that has been obtained from the observed frequency for one state shift to another. The study explores that regardless of the present status of API, in the long run, the index shows a probability of 0.9231 for a good state, and a moderate and unhealthy state with a probability of 0.0722 and 0.0037, while for very unhealthy and hazardous states a probability of 0.0001 and 0.0009. The outcome of this study reveals that the model development could be used as a forecasting method that able to help government to project a prevention action plan during hazy weather.

show abstract

Section: Discussionsupporting

confidence: 72%

Markov Chain Model Development for Forecasting Air Pollution Index of Miri, Sarawak

et al. 2019

View full text Add to dashboard Cite

show abstract

“…By applying Markov chain, the following set of equations is formulated to compute the state vectors in each time step (Hazra et al, 2017).…”

Section: Imperfect -Zeromentioning

confidence: 99%

“…Accordingly, T computes searchers' existence probabilities of the neighbour blocks and explores the one which has minimum searchers' existence probability. Now let us assume that q number of searchers are likely to share a block with non-zero searchers' existence probabilities in a particular time-step, where the individual existence probabilities for those searchers are represented as p 1 , p 2 ,…, p q , respectively, then, mutual searchers' existence probabilities (Hazra et al, 2017) in that particular block can be formulated as follows.…”

Section: Imperfect -Imperfectmentioning

confidence: 99%

Modelling and analysis of information-based target searching using mobile sensors

Hazra

Nene

Kumar

2018

IJSCC

Self Cite

View full text Add to dashboard Cite

Target searching is widely accepted as a significant area of study by various research communities. This paper addresses four target searching scenarios in a two-dimensional grid with obstacles, where multiple mobile sensors aim to search a single mobile target in a minimal time. The reachability condition of the target is checked before modelling the problem. The proposed work classifies the scenarios based on information-set available to the mobile sensors and the target. The scenarios are modelled as games that involve two adversary players: mobile sensor and target. The search strategies for the mobile sensors are formulated under different circumstances, and the strategic differences between cooperative and non-cooperative strategies are analysed. Later, the proposed work is extended in a new dimension, where information gain for the mobile sensors is determined by information refreshment interval. The proposed work helps the decision makers by facilitating the search operation in different scenarios.

show abstract

Modeling and Analysis of Grid-Based Target Searching Problems in a Mobile Sensor Network

Hazra

Kumar

Nene

2017

Wireless Pers Commun

View full text Add to dashboard Cite

A Strategic Framework for Searching Mobile Targets Using Mobile Sensors

Cited by 7 publications

References 20 publications

Markov Chain Model Development for Forecasting Air Pollution Index of Miri, Sarawak

Markov Chain Model Development for Forecasting Air Pollution Index of Miri, Sarawak

Modelling and analysis of information-based target searching using mobile sensors

Modeling and Analysis of Grid-Based Target Searching Problems in a Mobile Sensor Network

Contact Info

Product

Resources

About