Pub Date : 2017-10-01DOI: 10.1109/UEMCON.2017.8249094
S. Chatterjee, Shreya Chatterjee, Soumyadeep Chowdhury, S. Basak, Srijan Dey, Suparna Sain, K. S. Ghosal, N. Dalmia, Sachet Sircar
Modern technological advancement in sensors technology, miniaturization of devices and wireless networking facilitated the design and proliferation of wireless sensor networks by making it capable to monitor independently and controlling the ambience. One of the most important applications of sensor networks is for human health monitoring using minuscule wireless sensors, placed strategically on the human body, constitute a wireless network over the human body, termed as wireless body area network (WBAN) capable of administering various crucial implications and provides feedback on real-time basis to the user and supervising medical personnel. The Internet of Things (IoT) can be measured as the futuristic appraisal of the internet that realizes machine-to-machine learning and communication. As a result, IoT offers connectivity for everyone and everything. These two networks in integration provides connectivity between everything and anything. This paper is a study on integrated IoT and WBAN.
{"title":"Internet of Things and Body area network-an integrated future","authors":"S. Chatterjee, Shreya Chatterjee, Soumyadeep Chowdhury, S. Basak, Srijan Dey, Suparna Sain, K. S. Ghosal, N. Dalmia, Sachet Sircar","doi":"10.1109/UEMCON.2017.8249094","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249094","url":null,"abstract":"Modern technological advancement in sensors technology, miniaturization of devices and wireless networking facilitated the design and proliferation of wireless sensor networks by making it capable to monitor independently and controlling the ambience. One of the most important applications of sensor networks is for human health monitoring using minuscule wireless sensors, placed strategically on the human body, constitute a wireless network over the human body, termed as wireless body area network (WBAN) capable of administering various crucial implications and provides feedback on real-time basis to the user and supervising medical personnel. The Internet of Things (IoT) can be measured as the futuristic appraisal of the internet that realizes machine-to-machine learning and communication. As a result, IoT offers connectivity for everyone and everything. These two networks in integration provides connectivity between everything and anything. This paper is a study on integrated IoT and WBAN.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121712726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-10-01DOI: 10.1109/UEMCON.2017.8248970
M. Levy, D. Raviv
Given the mission critical nature of data centers, metrics must provide a holistic understanding of the data center behavior, which is only viable if risks to the actual operation are considered. This paper proposes a framework for measuring data center site risk. The proposed methodology is aimed at standardizing a process to help evaluate data center sites and compare them to each other, or compare different scenarios where the data center operates. It establishes a process under defined criteria for the site risk analysis, despite the fact that much of that criteria is based on standards and expert knowledge. The metric is also a way to communicate and better understand risks due to the data center location, and help evaluate mitigation strategies.
{"title":"A framework for data center site risk metric","authors":"M. Levy, D. Raviv","doi":"10.1109/UEMCON.2017.8248970","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8248970","url":null,"abstract":"Given the mission critical nature of data centers, metrics must provide a holistic understanding of the data center behavior, which is only viable if risks to the actual operation are considered. This paper proposes a framework for measuring data center site risk. The proposed methodology is aimed at standardizing a process to help evaluate data center sites and compare them to each other, or compare different scenarios where the data center operates. It establishes a process under defined criteria for the site risk analysis, despite the fact that much of that criteria is based on standards and expert knowledge. The metric is also a way to communicate and better understand risks due to the data center location, and help evaluate mitigation strategies.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134594813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-10-01DOI: 10.1109/UEMCON.2017.8249020
W. Contreras, S. Ziavras
Structural health monitoring (SHM) involves the development of strategies to assess the condition of instrumented engineering structures. One of the most critical applications of SHM systems is civil infrastructure. For this application, it is particularly important that SHM systems be inexpensive and easy to deploy, since the maintenance of infrastructure is often inadequately funded. Wireless sensor networks (WSN) can be very useful toward this end. We present an efficient WSN-based SHM algorithm for detecting, localizing, and monitoring the progression of damage in infrastructure applications. The algorithm utilizes a novel vibration-based pattern matching technique that is very well suited for low-power WSN nodes. During a training phase, a body of reference patterns is formed from vibrations observed at sensor nodes distributed throughout the structure. During the operational phase, observed patterns are compared to the reference patterns to determine if a match exists. Through the use of an innovative distributed algorithm, a time complexity of O(logN) is achieved for the matching process. If a match does not exist, potential damage is indicated and the reference pattern closest to the observed pattern is determined using Euclidean distance. The difference between the two patterns indicates the sensor nodes at which potential damage exists. Clusters are then formed around these sensor nodes in order to monitor the progression of local damage. Simulations are performed in MATLAB for a typical bridge deployment in order to determine the degree of overlapping that occurs as clusters are generated in response to potential damage. The simulations indicate that overlapping increases gracefully as the number of nodes experiencing damage increases.
{"title":"Efficient infrastructure damage detection and localization using wireless sensor networks, with cluster generation for monitoring damage progression","authors":"W. Contreras, S. Ziavras","doi":"10.1109/UEMCON.2017.8249020","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249020","url":null,"abstract":"Structural health monitoring (SHM) involves the development of strategies to assess the condition of instrumented engineering structures. One of the most critical applications of SHM systems is civil infrastructure. For this application, it is particularly important that SHM systems be inexpensive and easy to deploy, since the maintenance of infrastructure is often inadequately funded. Wireless sensor networks (WSN) can be very useful toward this end. We present an efficient WSN-based SHM algorithm for detecting, localizing, and monitoring the progression of damage in infrastructure applications. The algorithm utilizes a novel vibration-based pattern matching technique that is very well suited for low-power WSN nodes. During a training phase, a body of reference patterns is formed from vibrations observed at sensor nodes distributed throughout the structure. During the operational phase, observed patterns are compared to the reference patterns to determine if a match exists. Through the use of an innovative distributed algorithm, a time complexity of O(logN) is achieved for the matching process. If a match does not exist, potential damage is indicated and the reference pattern closest to the observed pattern is determined using Euclidean distance. The difference between the two patterns indicates the sensor nodes at which potential damage exists. Clusters are then formed around these sensor nodes in order to monitor the progression of local damage. Simulations are performed in MATLAB for a typical bridge deployment in order to determine the degree of overlapping that occurs as clusters are generated in response to potential damage. The simulations indicate that overlapping increases gracefully as the number of nodes experiencing damage increases.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131046382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-10-01DOI: 10.1109/UEMCON.2017.8249069
S. Moballegh, B. Sirkeci-Mergen
In this paper, we study the power efficiency of cooperative broadcast in high-density 1D networks. This is an extension to our previous work which assumed interference-free transmission using single-shot transmission protocol [1]. In this work, interference is taken into account by introducing continuous source transmission protocol. Sufficient condition for successful broadcast is derived and the power consumption is compared with noncooperative multihop broadcast. Received signals are assumed to be attenuated under simple deterministic pathloss such that the pathloss exponent is greater than 1. We use continuum approximation which effectively models high node-density networks. We compare cooperative and noncooperative broadcast in terms of power efficiency and show that cooperative broadcast is more efficient under the bidirectional transmission while it is power inefficient if the transmission is unidirectional.
{"title":"Power efficiency of cooperative broadcast in 1-D dense networks with interference","authors":"S. Moballegh, B. Sirkeci-Mergen","doi":"10.1109/UEMCON.2017.8249069","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249069","url":null,"abstract":"In this paper, we study the power efficiency of cooperative broadcast in high-density 1D networks. This is an extension to our previous work which assumed interference-free transmission using single-shot transmission protocol [1]. In this work, interference is taken into account by introducing continuous source transmission protocol. Sufficient condition for successful broadcast is derived and the power consumption is compared with noncooperative multihop broadcast. Received signals are assumed to be attenuated under simple deterministic pathloss such that the pathloss exponent is greater than 1. We use continuum approximation which effectively models high node-density networks. We compare cooperative and noncooperative broadcast in terms of power efficiency and show that cooperative broadcast is more efficient under the bidirectional transmission while it is power inefficient if the transmission is unidirectional.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131187520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-10-01DOI: 10.1109/UEMCON.2017.8249111
Qingxue Zhang, Dian Zhou, Xuan Zeng
Focusing on the privacy and security challenges brought by emerging/promising smart health applications, we propose a single-arm-ECG biometric human identification system, with two major contributions. Firstly, to replace the traditional inconvenient/uncomfortable ECG leads like the chest and two-wrist lead configurations, we propose a highly wearable single-arm-ECG lead configuration. Secondly, to prevent time-consuming and information-missing feature engineering work, we introduce advanced deep learning techniques to automatically learn from the raw ECG data highly level features. To achieve this goal, the 1D ECG time series is transform to a new domain, where a 2D ECG representation is obtained. Afterwards, a convolutional neural network is applied to the 2D ECG data and learn the hidden patterns for user identification purpose. The proposed system is validated on a single-arm-ECG dataset. This study demonstrates the feasibility of this highly wearable deep learning-empowered human identification system.
{"title":"PulsePrint: Single-arm-ECG biometric human identification using deep learning","authors":"Qingxue Zhang, Dian Zhou, Xuan Zeng","doi":"10.1109/UEMCON.2017.8249111","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249111","url":null,"abstract":"Focusing on the privacy and security challenges brought by emerging/promising smart health applications, we propose a single-arm-ECG biometric human identification system, with two major contributions. Firstly, to replace the traditional inconvenient/uncomfortable ECG leads like the chest and two-wrist lead configurations, we propose a highly wearable single-arm-ECG lead configuration. Secondly, to prevent time-consuming and information-missing feature engineering work, we introduce advanced deep learning techniques to automatically learn from the raw ECG data highly level features. To achieve this goal, the 1D ECG time series is transform to a new domain, where a 2D ECG representation is obtained. Afterwards, a convolutional neural network is applied to the 2D ECG data and learn the hidden patterns for user identification purpose. The proposed system is validated on a single-arm-ECG dataset. This study demonstrates the feasibility of this highly wearable deep learning-empowered human identification system.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"20 21","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132748347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-10-01DOI: 10.1109/UEMCON.2017.8249052
Daniel Gonzalez, T. Hayajneh
Crypto-ransomware is a challenging threat that ciphers a user's files while hiding the decryption key until a ransom is paid by the victim. This type of malware is a lucrative business for cybercriminals, generating millions of dollars annually. The spread of ransomware is increasing as traditional detection-based protection, such as antivirus and anti-malware, has proven ineffective at preventing attacks. Additionally, this form of malware is incorporating advanced encryption algorithms and expanding the number of file types it targets. Cybercriminals have found a lucrative market and no one is safe from being the next victim. Encrypting ransomware targets business small and large as well as the regular home user. This paper discusses ransomware methods of infection, technology behind it and what can be done to help prevent becoming the next victim. The paper investigates the most common types of crypto-ransomware, various payload methods of infection, typical behavior of crypto ransomware, its tactics, how an attack is ordinarily carried out, what files are most commonly targeted on a victim's computer, and recommendations for prevention and safeguards are listed as well.
{"title":"Detection and prevention of crypto-ransomware","authors":"Daniel Gonzalez, T. Hayajneh","doi":"10.1109/UEMCON.2017.8249052","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249052","url":null,"abstract":"Crypto-ransomware is a challenging threat that ciphers a user's files while hiding the decryption key until a ransom is paid by the victim. This type of malware is a lucrative business for cybercriminals, generating millions of dollars annually. The spread of ransomware is increasing as traditional detection-based protection, such as antivirus and anti-malware, has proven ineffective at preventing attacks. Additionally, this form of malware is incorporating advanced encryption algorithms and expanding the number of file types it targets. Cybercriminals have found a lucrative market and no one is safe from being the next victim. Encrypting ransomware targets business small and large as well as the regular home user. This paper discusses ransomware methods of infection, technology behind it and what can be done to help prevent becoming the next victim. The paper investigates the most common types of crypto-ransomware, various payload methods of infection, typical behavior of crypto ransomware, its tactics, how an attack is ordinarily carried out, what files are most commonly targeted on a victim's computer, and recommendations for prevention and safeguards are listed as well.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116001053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cellular image analysis is considered one of the important job in biomedical image analysis. Analysis of cellular images obtained using a microscope is necessary in various disciplines including engineering and medical imaging. Cell detection is necessary in various jobs of microscopic analysis that helps physicians to diagnose and extract features. Accurate identification of cells is necessary for precise diagnosis. Analysis methods based on morphology is one of the major research area and also useful in biomedical image analysis as well as in bioinformatics. Morphology based analysis acts as the helping hand for physicians. Morphology based analysis methods are useful in determining cell shape, irregularity, feature extraction and classification. In this work, some of the methods have been reported which can be helpful in analyzing some practical problem by employing a suitable technique.
{"title":"Cellular image processing using morphological analysis","authors":"Mousomi Roy, Shouvik Chakraborty, Kalyani Mali, Sankhadeep Chatterjee, Soumen Banerjee, Shibjit Mitra, Rajarshee Naskar, Anubhab Bhattacharjee","doi":"10.1109/UEMCON.2017.8249037","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249037","url":null,"abstract":"Cellular image analysis is considered one of the important job in biomedical image analysis. Analysis of cellular images obtained using a microscope is necessary in various disciplines including engineering and medical imaging. Cell detection is necessary in various jobs of microscopic analysis that helps physicians to diagnose and extract features. Accurate identification of cells is necessary for precise diagnosis. Analysis methods based on morphology is one of the major research area and also useful in biomedical image analysis as well as in bioinformatics. Morphology based analysis acts as the helping hand for physicians. Morphology based analysis methods are useful in determining cell shape, irregularity, feature extraction and classification. In this work, some of the methods have been reported which can be helpful in analyzing some practical problem by employing a suitable technique.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117084323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-10-01DOI: 10.1109/UEMCON.2017.8249039
P. Bradford
Consider any two vertices in a weighted digraph. The exact path length problem is to determine if there is a path of a given fixed cost between these vertices. This paper focuses on the exact path problem for costs −1,0 or +1 between all pairs of vertices. This special case is also restricted to original edge weights from {−1, +1}. In this special case, this paper gives an O(nω log2 n) exact path solution, where ω is the best exponent for matrix multiplication. Basic variations of this algorithm determine which pairs of digraph nodes have Dyck or semi-Dyck labeled paths between them, assuming two terminals or parenthesis. Therefore, determining reachability for Dyck or semi-Dyck labeled paths costs O(nω log2 n). Both the exact path and labeled path solutions can be improved by poly-log factors, but these improvements are not given here. To find Dyck or semi-Dyck reachability in a labeled digraph, each edge has a single symbol on it. A path label is made by concatenating all symbols along the path. Cycles are allowed without any repeated edges. These paths have the same number of balanced parenthesizations (semi-Dyck languages) or have an equal numbers of matching symbols (Dyck languages). The exact path length problem has many applications. These applications include the labeled path problems given here, which in turn, have many applications.
{"title":"Efficient exact paths for dyck and semi-dyck labeled path reachability (extended abstract)","authors":"P. Bradford","doi":"10.1109/UEMCON.2017.8249039","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249039","url":null,"abstract":"Consider any two vertices in a weighted digraph. The exact path length problem is to determine if there is a path of a given fixed cost between these vertices. This paper focuses on the exact path problem for costs −1,0 or +1 between all pairs of vertices. This special case is also restricted to original edge weights from {−1, +1}. In this special case, this paper gives an O(nω log2 n) exact path solution, where ω is the best exponent for matrix multiplication. Basic variations of this algorithm determine which pairs of digraph nodes have Dyck or semi-Dyck labeled paths between them, assuming two terminals or parenthesis. Therefore, determining reachability for Dyck or semi-Dyck labeled paths costs O(nω log2 n). Both the exact path and labeled path solutions can be improved by poly-log factors, but these improvements are not given here. To find Dyck or semi-Dyck reachability in a labeled digraph, each edge has a single symbol on it. A path label is made by concatenating all symbols along the path. Cycles are allowed without any repeated edges. These paths have the same number of balanced parenthesizations (semi-Dyck languages) or have an equal numbers of matching symbols (Dyck languages). The exact path length problem has many applications. These applications include the labeled path problems given here, which in turn, have many applications.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130034303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-10-01DOI: 10.1109/UEMCON.2017.8249027
Shekufeh Shafeie, M. Meybodi
The scheduling of nodes in a wireless Ad Hoc Sensor network is getting them to alternate between the sleeping and active mode. If this process of adjusting the wake/sleep schedule of all nodes that is, a topology management mechanism, is maintained in an optimal manner, further energy can be saved, which will have a direct impact on prolonging the lifetime of the network. So, in this paper a distributed power saving coordination algorithm for multi-hop ad hoc wireless networks based on learning automata without significantly diminishing the quality of services of the network such as capacity or connectivity of the network is proposed such that all nodes in the network that are equipped with learning automata don't need to be synchronized with each other. Learning automata abilities such as low computational load, usability in distributed environments with ambiguous information, and adaptability to changes via low environmental feedbacks are all, factors that can provide the mentioned optimal manner for nodes; and cause better fitness with local techniques in ad hoc wireless networks. The proposed protocol, SpanLAQ, consists of two phases: coordinator announcement and coordinator withdrawal which are based on learning automata to ensure fairness, and to make local decisions on whether a node is going to sleep or joining to a forwarding backbone as a coordinator. So, using learning automata with passing of time causes a decrease in energy consumption and an improvement of the network lifetime in SpanLAQ protocol with an 802.11 network in power saving mode, in comparison to other similar protocols such as Span and without any topology management protocols. Simulation results with a practical energy model also show that the above result is being achieved with some improvements in capacity, connectivity and communication latency.
{"title":"Topology maintenance of Ad Hoc wireless sensor networks with an optimum distributed power saving scheduling learning automata based algorithm","authors":"Shekufeh Shafeie, M. Meybodi","doi":"10.1109/UEMCON.2017.8249027","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249027","url":null,"abstract":"The scheduling of nodes in a wireless Ad Hoc Sensor network is getting them to alternate between the sleeping and active mode. If this process of adjusting the wake/sleep schedule of all nodes that is, a topology management mechanism, is maintained in an optimal manner, further energy can be saved, which will have a direct impact on prolonging the lifetime of the network. So, in this paper a distributed power saving coordination algorithm for multi-hop ad hoc wireless networks based on learning automata without significantly diminishing the quality of services of the network such as capacity or connectivity of the network is proposed such that all nodes in the network that are equipped with learning automata don't need to be synchronized with each other. Learning automata abilities such as low computational load, usability in distributed environments with ambiguous information, and adaptability to changes via low environmental feedbacks are all, factors that can provide the mentioned optimal manner for nodes; and cause better fitness with local techniques in ad hoc wireless networks. The proposed protocol, SpanLAQ, consists of two phases: coordinator announcement and coordinator withdrawal which are based on learning automata to ensure fairness, and to make local decisions on whether a node is going to sleep or joining to a forwarding backbone as a coordinator. So, using learning automata with passing of time causes a decrease in energy consumption and an improvement of the network lifetime in SpanLAQ protocol with an 802.11 network in power saving mode, in comparison to other similar protocols such as Span and without any topology management protocols. Simulation results with a practical energy model also show that the above result is being achieved with some improvements in capacity, connectivity and communication latency.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129159920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The current work proposes a neural based detection method of two different skin diseases using skin imaging. Skin images of two diseases namely Basel Cell Carcinoma and Skin Angioma are utilized. SIFT feature extractor has been employed followed by a clustering phase on feature space in order to reduce the number of features suitable for neural based models. The extracted bag-of-features modified dataset is used to train metaheuristic supported hybrid Artificial Neural Networks to classify the skin images in order to detect the diseases under study. A well-known multi objective optimization technique called Non-dominated Sorting Genetic Algorithm — II is used to train the ANN (NN-NSGA-II). The proposed model is further compared with two other well-known metaheuristic based classifier namely NN-PSO (ANN trained with PSO) and NN-CS (ANN trained with Cuckoo Search) in terms of testing phase confusion matrix based performance measuring metrics such as accuracy, precision, recall and F-measure. Experimental results indicated towards the superiority of the proposed bag-of-features enabled NN-NSGA-II model.
{"title":"Image based skin disease detection using hybrid neural network coupled bag-of-features","authors":"Shouvik Chakraborty, Kalyani Mali, Sankhadeep Chatterjee, Sumit Anand, Aavery Basu, Soumen Banerjee, Mitali Das, Abhishek Bhattacharya","doi":"10.1109/UEMCON.2017.8249038","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249038","url":null,"abstract":"The current work proposes a neural based detection method of two different skin diseases using skin imaging. Skin images of two diseases namely Basel Cell Carcinoma and Skin Angioma are utilized. SIFT feature extractor has been employed followed by a clustering phase on feature space in order to reduce the number of features suitable for neural based models. The extracted bag-of-features modified dataset is used to train metaheuristic supported hybrid Artificial Neural Networks to classify the skin images in order to detect the diseases under study. A well-known multi objective optimization technique called Non-dominated Sorting Genetic Algorithm — II is used to train the ANN (NN-NSGA-II). The proposed model is further compared with two other well-known metaheuristic based classifier namely NN-PSO (ANN trained with PSO) and NN-CS (ANN trained with Cuckoo Search) in terms of testing phase confusion matrix based performance measuring metrics such as accuracy, precision, recall and F-measure. Experimental results indicated towards the superiority of the proposed bag-of-features enabled NN-NSGA-II model.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132322104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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