Bio-inspired network security for 5G-enabled IoT applications
- Authors: Saleem, Kashif , Alabduljabbar, Ghadah , Alrowais, Nouf , Al-Muhtadi, Jalal , Imran, Muhammad , Rodrigues, Joel
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE access Vol. 8, no. (2020), p. 1-1
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- Description: Every IPv6-enabled device connected and communicating over the Internet forms the Internet of things (IoT) that is prevalent in society and is used in daily life. This IoT platform will quickly grow to be populated with billions or more objects by making every electrical appliance, car, and even items of furniture smart and connected. The 5th generation (5G) and beyond networks will further boost these IoT systems. The massive utilization of these systems over gigabits per second generates numerous issues. Owing to the huge complexity in large-scale deployment of IoT, data privacy and security are the most prominent challenges, especially for critical applications such as Industry 4.0, e-healthcare, and military. Threat agents persistently strive to find new vulnerabilities and exploit them. Therefore, including promising security measures to support the running systems, not to harm or collapse them, is essential. Nature-inspired algorithms have the capability to provide autonomous and sustainable defense and healing mechanisms. This paper first surveys the 5G network layer security for IoT applications and lists the network layer security vulnerabilities and requirements in wireless sensor networks, IoT, and 5G-enabled IoT. Second, a detailed literature review is conducted with the current network layer security methods and the bio-inspired techniques for IoT applications exchanging data packets over 5G. Finally, the bio-inspired algorithms are analyzed in the context of providing a secure network layer for IoT applications connected over 5G and beyond networks.
Detectability and uniform global asymptotic stability in switched nonlinear time-varying systems
- Authors: Lee, Ti-Chung , Tan, Ying , Mareels, Iven
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE transactions on automatic control Vol. 65, no. 5 (2020), p. 2123-2138
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- Description: This paper employs detectability ideas to decide uniform global asymptotic stability (UGAS) of the trivial solution for a class of switched nonlinear time-varying systems when the trivial solution is uniformly globally stable. Using the notion of limiting behaviors of the state, output, and switching signals, the concept of a limiting zeroing-output solution is introduced. This leads to a definition of weak zero-state detectability (WZSD) that can be used to check UGAS, (uniformly for a set of switched signals). En route to establish this, a number of new stability results are derived. For example, under appropriate conditions, it is feasible to decide UGAS even when the switching signal does not satisfy an averaged dwell-time condition. It is also shown that WZSD of the original switched system can be verified by detectability conditions of much simpler auxiliary systems. Moreover, UGAS can be guaranteed without requiring that in each allowable system (without switching), the trivial solution is attractive. The effectiveness of the proposed concept is illustrated by a few examples including a switched semi-quasi-Z-source inverter.
Energy efficient elliptical concave visibility graph algorithm for unmanned aerial vehicle in an obstacle-rich environment
- Authors: Debnath, Sanjoy , Omar, Rosli , Bagchi, Susama , Nafea, Marwan , Naha, Ranesh , Nadira Sabudin, Elia
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2020 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS); Shah Alam, Malaysia;20 June 2020 p. 129-134
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- Description: This paper proposes a path planning algorithm for unmanned aerial vehicle (UAV) called Elliptical Concave Visibility Graph (ECoVG). The algorithm, which is based on visibility graph (VG), overcomes the limitations of VG computation time and hence, it can be applied in real-time and in obstacle-rich environments. An experimental investigation has been done to compare the performance between ECoVG and another VG based method namely Equilateral-Space Oriented VG (ESOVG) in terms of computational time and path length. The investigation was done in identical scenarios through simulation to show that the ECoVG has a better computation time than that of ESOVG for its efficient selection of a region in calculating the path. It is also found that the proposed algorithm is energy efficient and complete since it can find a path if one exists.
Energy storage as a service: Optimal pricing for transmission congestion relief
- Authors: Arteaga, Juan , Zareipour, Hamidreza , Amjady, Nima
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE open access journal of power and energy Vol. 7, no. (2020), p. 514-523
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- Description: This paper focuses on pricing Energy Storage as a Service (ESaaS) for Transmission congestion relief (TCR). We consider a merchant storage facility that competes in an electricity market to trade energy and ancillary services on a day-to-day basis. The facility also has the opportunity to provide a firm TCR service to a regional network operator under a long-term contract. Providing the additional TCR service would impose limitations on the facility's ability to fully harvest daily market trade opportunities. Thus, we model the opportunity costs associated with the TCR service and use it in a hybrid cost-value customized pricing technique to determine the risk-constrained optimal price of ESaaS for TCR. Given the long-term nature of the commitment to provide the TCR service, we use the Conditional Value at Risk (CVaR) metric to mitigate the long-term financial risks faced by the facility. The proposed pricing strategy enables the storage owner to estimate the additional financial gains and the associated risks that would likely result from adding the new service to its operation. Numerical simulations are provided to support the proposed methodology.
Extremum seeking control with sporadic packet transmission for networked control systems
- Authors: Premaratne, Upeka , Halgamuge, Saman , Tan, Ying , Mareels, Iven
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE transactions on control of network systems Vol. 7, no. 2 (2020), p. 758-769
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- Description: Extremum seeking control (ESC) is a data-driven optimization technique that can steer a dynamic plant toward an extremum of an unknown but measurable input to steady-state output. In the context of networked control systems, a new implementation method for ESC inspired by the well-known Luus-Jaakola algorithm is proposed. The main motivation is to minimize the communication burden associated with the search phase of ESC. In the proposed method, the controller only requires a notification of a change registered at the sensor, rather than the full information available at the sensor. This event-based approach leads to sporadic packet transmission. In addition, the proposed method is able to directly account for constraints while seeking the desired extremum. The constraints may be of the inequality or equality type. The algorithm's behavior is illustrated on a networked water pump control system.
Machine learning-based modelling for museum visitations prediction
- Authors: Yap, Norman , Gong, Mingwei , Naha, Ranesh , Mahanti, Aniket
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2020 International Symposium on Networks, Computers and Communications (ISNCC); Montreal, Canada; 20-22nd October, 2020, p.1-7
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- Description: Cultural venues like museums increasingly seek to harness the value of data analytics to make data driven decisions related to exhibitions duration, marketing campaigns, resource planning, and revenue optimization. One key priority is the need to understand the influencing factors behind visitor attendance. Using data collected from a large museum, we investigated whether the weather has a significant impact on visitor attendance or that other factors are more important. We applied the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology to perform the research, developed and built four different types of regression models using R and its machine learning packages to model visitor attendance. The models were trained and evaluated. Predictions of visitor attendance were then generated from each of the four models and forecast accuracy was measured. The extreme gradient boost model was the best model with the highest average forecast accuracy of 93% and lowest forecast variability when benchmarked against the actual visitor attendance from the test data set. The weather was not considered to be as significant in predicting visitor trends and numbers to the museum compared to factors like time of the day, day of the week and school holidays. However, it was still measured to have a slight impact as excluding weather variables resulted in a model with a poorer fit. Weather can potentially have a more marked impact on cultural attractions in more extreme weather environments and outdoor venues.
Piracy on the internet: Publisher-side analysis on file hosting services
- Authors: Chan, Marcus , Gong, Mingwei , Naha, Ranesh , Mahanti, Aniket
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2020 International Symposium on Networks, Computers and Communications (ISNCC); Montreal, QC, Canada; 20-22 October 2020 p. 1-7
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- Description: In the file sharing ecosystem, One-Click File Hosting Services (FHS) such as Rapidgator and Uploaded, the previously Rapidshare and Megaupload, provide a platform for users to share copyrighted content. We present a publisher-side analysis of FHS file sharing dynamics through data collected from active measurement by crawling Warez-BB. The website is essentially a forum where publishers can share links to content they have uploaded on file hosting services. Consumers can use the website to gain access to content shared on the website, often free of charge. We primarily analyse various characteristics of file sharing with respect to view count as the evaluation metric.
Robust resiliency-oriented operation of active distribution networks considering windstorms
- Authors: Esfahani, Moein , Amjady, Nima , Bagheri, Bahareh , Hatziargyriou, Nikos
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE transactions on power systems Vol. 35, no. 5 (2020), p. 3481-3493
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- Description: Recent climate changes have created intense natural disasters, such as windstorms, which can cause significant damages to power grids. System resilience is defined as the ability of the system to withstand such high-impact low-probability events. This paper proposes a robust resilient operational schedule for active distribution networks against windstorms. In order to capture dynamic behaviors of these disasters, zonal disaster-specific uncertainty sets associated with the windstorm are proposed. Additionally, the unavailability uncertainties of N-K contingencies as well as the forecast uncertainties of load demand, wind power, and solar power are taken into account. Instead of committing micro-turbines and energy storage systems in the first stage ( here-and-now ) of the decision-making process, the proposed model considers these commitment decisions in the second stage ( wait-and-see ) of the decision-making process, which is more consistent with the fast response time of these units. Since the second stage of the proposed model has binary decision variables, recent KKT-based and duality-based methods are not applicable. Therefore, a new solution method based on block coordinate descent (BCD) and line search (LS) techniques is proposed to solve the bi-level problem. Eventually, IEEE 33-bus distribution test system is used to illustrate the effectiveness of the proposed model and solution method.
Short-circuit constrained power system expansion planning considering bundling and voltage levels of lines
- Authors: Esmaili, Masoud , Ghamsari-Yazdel, Mohammad , Amjady, Nima , Conejo, Antonio
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE transactions on power systems Vol. 35, no. 1 (2020), p. 584-593
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- Description: System expansion planning (SEP) models do not generally represent voltage levels, bundled conductors in transmission lines, and short-circuit limits. These modeling assumptions may result in suboptimal planning outcomes. To overcome this potential flaw, we propose a short-circuit constrained dynamic SEP (SC-SEP) that allows for investment decisions at different stages of the planning horizon and includes a detailed representation of voltage levels, alternative bundled conductor options per line and short-circuit limits. An effective linearization technique is used to transform the resulting mixed-integer nonlinear model into a mixed-integer linear one. The accuracy of the proposed model and the effectiveness of the proposed linearization technique are illustrated using the IEEE 39-bus system.
THCluster: herb supplements categorization for precision traditional Chinese medicine
- Authors: Ruan, Chunyang , Wang, Ye , Zhang, Yanchun , Ma, Jiangang , Chen, Huijuan , Aickelin, Uwe , Zhu, Shanfeng , Zhang, Ting
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);Kansas City, MO, USA; 13-16 Nov. 2017 p. 417-424
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- Description: There has been a continuing demand for traditional and complementary medicine worldwide. A fundamental and important topic in Traditional Chinese Medicine (TCM) is to optimize the prescription and to detect herb regularities from TCM data. In this paper, we propose a novel clustering model to solve this general problem of herb categorization, a pivotal task of prescription optimization and herb regularities. The model utilizes Random Walks method, Bayesian rules and Expectation Maximization(EM) models to complete a clustering analysis effectively on a heterogeneous information network. We performed extensive experiments on the real-world datasets and compared our method with other algorithms and experts. Experimental results have demonstrated the effectiveness of the proposed model for discovering useful categorization of herbs and its potential clinical manifestations.
Transmission expansion planning including tcscs and sfcls: A minlp approach
- Authors: Esmaili, Masoud , Ghamsari-Yazdel, Mohammad , Amjady, Nima , Chung, C. Y. , Conejo, Antonio
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE transactions on power systems Vol. 35, no. 6 (2020), p. 4396-4407
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- Description: We propose a transmission expansion planning model that integrates thyristor-controlled series compensators (TCSCs) to enhance line transmission capacity, and superconducting fault current limiters (SFCLs) to control short-circuit levels. The harmonious interplay between TCSCs and SFCLs results in effective and economically attractive optimal expansion plans. This multi-stage planning model translates into a complex mixed-integer nonlinear programming problem, which is hard to solve. To solve it, we propose a successive linearization technique within a Benders' decomposition scheme that proves effective in finding optimal solutions and efficient in terms of computational burden. We illustrate the methodology proposed using the IEEE 39-bus system.
VPP self-scheduling strategy using multi-horizon igdt, enhanced normalized normal constraint, and bi-directional decision-making approach
- Authors: Yazdaninejad, Mohsen , Amjady, Nima , Dehghan, Shahab
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE transactions on smart grid Vol. 11, no. 4 (2020), p. 3632-3645
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- Description: This paper presents a new robust self-scheduling strategy for virtual power plants (VPPs) considering the uncertainty sources of electricity prices, wind generations, and loads. Multi-horizon information-gap decision theory (MH-IGDT) as a non-deterministic and non-probabilistic uncertainty modeling framework is proposed here to specifically model the uncertainty sources considering their various uncertainty horizons. Since each uncertain parameter tends to optimize its uncertainty horizon competitively for a particular value of the uncertainty budget, the proposed MH-IGDT model is formulated as a multi-objective optimization problem. To solve this multi-objective problem, enhanced normalized normal constraint (ENNC) method is presented, which can obtain efficient uniformly-distributed Pareto optimal solutions. The proposed ENNC includes augmented normalized normal constraint method and lexicographic optimization technique to enhance the search performance in the objective space. To address the unsolved issue of being risk-averse or risk-seeker for a VPP in the market, a bi-directional decision-making approach is presented. This decision maker comprises an ex-ante performance evaluation method and a forward-backward dynamic programming approach to hourly find the best Pareto solution within the generated risk-averse and risk-seeker Pareto frontiers. Simulation results of the proposed self-scheduling strategy are presented for a VPP including dispatchable/non-dispatchable units, storages, and loads.
A Comprehensive protection method for securing the organization's network against cyberattacks
- Authors: Kbar, Ghassan , Alazab, Ammar
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 Cybersecurity and Cyberforensics Conference (CCC); Melbourne, VIC, Australia; 8-9 May 2019 p. 118-122
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- Description: The advance in technologies helped in providing efficient system that connect people worldwide such as the use of internet. At the same time cyber attackers exploited the vulnerabilities existed in these technologies to conduct large variety of attack activities against the information and systems. Researchers and solution's providers implemented different countermeasure mechanisms to protect the system against attacks and saved the discovered type of attack in attack database for future analysis and decision. Intrusion Detection (ID) system is an example for protecting the system against attacks by monitoring the network activities and updating the attack database for future analysis and protection decision. In addition to IDs, firewall, intrusion prevention, encryption, authorization and authentication are used to protect the system. Furthermore, a supplementary configurations honeypot systems can be used to strengthen the system security.
- Description: The advance in technologies helped in providing efficient system that connect people worldwide such as the use of internet. At the same time cyber attackers exploited the vulnerabilities existed in these technologies to conduct large variety of attack activities against the information and systems. Researchers and solution's providers implemented different countermeasure mechanisms to protect the system against at-tacks and saved the discovered type of attack in attack database for future analysis and decision. Intrusion Detection (ID) system is an example for protecting the system against attacks by monitoring the network activities and updating the attack data-base for future analysis and protection decision. In addition to IDs, firewall, intrusion prevention, encryption, authorization and authentication are used to protect the sys-tem. Furthermore, a supplementary configurations honeypot systems can be used to strengthen the system security.
A Hybrid estimation and identification method for online calculation of voltage-dependent load parameters
- Authors: Kabiri, Mehdi , Amjady, Nima
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE systems journal Vol. 13, no. 1 (2019), p. 792-801
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- Description: This paper presents a hybrid estimation and identification (HEI) method for identifying the load model parameters of voltage-dependent loads along with estimating the system states. The parameters of voltage-dependent load models are identified using multiple snapshots consecutively gathered through supervisory control and data acquisition system. The proposed HEI method imposes no additional cost to the installed metering devices or communication network. Mathematical aspects of the proposed HEI method are analytically proved and a four-step approach is presented for implementing it. Comprehensive numerical experiments and statistical analysis are presented to demonstrate the effectiveness of the proposed method for both load model identification and state estimation.
A Magnetic linked modular cascaded multilevel converter for medium voltage grid applications
- Authors: Hasan,Md Mubashwar , Islam, Syed , Abu-Siada, Ahmed , Islam, Rabiul Md
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 29th Australasian Universities Power Engineering Conference (AUPEC); Nadi, Fiji; 26-29 November 2019
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- Description: One of the key advantages of cascaded multilevel inverters (CMLI) is their ability to generate medium voltage output by using low voltage rated circuit components. For this reason, CMLI has been given much attention in renewable and industrial applications. However, in spite CMLI advantages, balanced input dc voltage management at the cascaded cells is still considered one of the main drawbacks, which limits its straightforward applications. Moreover, galvanic isolation between the input dc supply and the inverter output voltage is essential for grid-connected application. In such case, a step-up transformer is utilized between the inverter output terminals and the grid. This solution incurs additional cost, increases implementation size, weight and maintenance. In this paper, a CMLI is proposed for medium voltage applications by utilizing high frequency magnetic link to ensure galvanic isolation without the need to a conventional step-up transformer as per the current practice. 3 rd harmonic-injected sine pulse width modulation strategy is adopted as a switching controller for the proposed cascaded inverter that is implemented and tested. Experimental results attest the simulation results and confirm the feasibility of the proposed inverter
A Reinforcement learning based algorithm towards energy efficient 5G Multi-tier network
- Authors: Islam, Nahina , Alazab, Ammar , Alazab, Mamoun
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 Cybersecurity and Cyberforensics Conference (CCC); Melbourne, Vic; 8th-9th May, 2019 p. 96-101
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- Description: Energy efficiency is a key factor in the next generation wireless communication systems. Sleep mode implementation in multi-tier 5G networks has proven to be a very good approach for improving the energy efficiency. In this paper, we propose a novel reinforcement learning based decision making algorithm to implement sleep mode in the base stations (BSs) used in multi-tier 5G networks. We propose a Markovian Decision process (MDP) based algorithm to switch between three different power consumption modes of a BS for improving the energy efficiency of the 5G network. The MDP based approach intelligently switches between the states of the BS based on the offered traffic whilst maintaining a prescribed minimum channel rate per user. Our results show that there is a significant gain in the energy efficiency when using our proposed MDP algorithm together with the three-state BSs. We have also shown the energy-delay tradeoff in order to design a delay aware network.
A Rotation invariant HOG descriptor for tire pattern image classification
- Authors: Liu, Ying , Ge, Yuxiang , Wang, Fuping , Liu, Qiqi , Lei, Yanbo , Zhang, Dengsheng , Lu, Guojun
- Date: 2019
- Type: Text , Conference proceedings
- Relation: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); Brighton, UK, 12-17 May 2019. p. 2412-2416
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- Description: Texture feature is important in describing tire pattern image which provides useful clue in solving crime cases and traffic accidents. In this paper, we propose a novel texture feature extraction method based on HOG (Histogram of Oriented Gradient) and dominant gradient (DG) in tire pattern images, named HOG-DG. The proposed HOG-DG is not only robust to illumination and scale changes but also is rotation-invariant. In the proposed HOG-DG, HOG features are first computed from circular local cells, and HOG features from an image are concatenated and normalized using the DG to construct the HOG-DG feature. HOG-DG is used to train a support-vector-machine (SVM) classifier for tire pattern classification. Experimental results demonstrate its outstanding performance for tire pattern description.
Acoustic sensor networks and mobile robotics for sound source localization
- Authors: Nguyen, Linh , Miro, Jaime Valls
- Date: 2019
- Type: Text , Conference proceedings
- Relation: IEEE 15th International Conference on Control and Automation (ICCA);Edinburgh, UK; 16-19 July 2019 p. 1453-1458
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- Description: Localizing a sound source is a fundamental but still challenging issue in many applications, where sound information is gathered by static and local microphone sensors. Therefore, this work proposes a new system by exploiting advances in sensor networks and robotics to more accurately address the problem of sound source localization. By the use of the network infrastructure, acoustic sensors are more efficient to spatially monitor acoustical phenomena. Furthermore, a mobile robot is proposed to carry an extra microphone array in order to collect more acoustic signals when it travels around the environment. Driving the robot is guided by the need to increase the quality of the data gathered by the static acoustic sensors, which leads to better probabilistic fusion of all the information gained, so that an increasingly accurate map of the sound source can be built. The proposed system has been validated in a real-life environment, where the obtained results are highly promising.
Affinely adjustable robust bidding strategy for a solar plant paired with a battery storage
- Authors: Attarha, Ahmad , Amjady, Nima , Dehghan, Shahab
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE transactions on smart grid Vol. 10, no. 3 (2019), p. 2629-2640
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- Description: Due to green power generation, large solar plants with capacities in the order of several MWs are increasingly installed worldwide and plenty of these plants participate in electricity markets. However, the uncertain production of a solar plant poses a noticeable risk to the profit of a solar producer. To overcome the associated risk, storage systems have been recently paired with solar plants. Not only storage systems can increase the reliability of photovoltaic (PV) solar power, but also can increase economic benefits through energy arbitrage in an electricity market. Although paring a storage system with a solar plant is an economically promising combination, a proper bidding strategy model, which appropriately characterizes the uncertainties of both PV solar power productions and electricity prices, is essential. To do so, this paper proposes an affinely adjustable robust bidding strategy for a solar producer paired with a battery storage system. The uncertainties associated with solar power productions and electricity prices are characterized through bounded intervals in a controllable polyhedral uncertainty set. Affine functions are used to solve the proposed model directly without decomposition. Numerical results illustrate the effectiveness of the proposed method.
Can a robot hear the shape and dimensions of a room?
- Authors: Nguyen, Linh , Miro, Jaime Valls , Qiu, Xiaojun
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); Macau, China; 03-08 November 2019 p. 5346-5351
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- Description: Knowing the geometry of a space is desirable for many applications, e.g. sound source localization, sound field reproduction or auralization. In circumstances where only acoustic signals can be obtained, estimating the geometry of a room is a challenging proposition. Existing methods have been proposed to reconstruct a room from the room impulse responses (RIRs). However, the sound source and microphones must be deployed in a feasible region of the room for it to work, which is impractical when the room is unknown. This work propose to employ a robot equipped with a sound source and four acoustic sensors, to follow a proposed path planning strategy to moves around the room to collect first image sources for room geometry estimation. The strategy can effectively drives the robot from a random initial location through the room so that the room geometry is guaranteed to be revealed. Effectiveness of the proposed approach is extensively validated in a synthetic environment, where the results obtained are highly promising.