varunr14/newsummary huggingface dataset:
In this paper a distributed base station cooperation based handover management method is proposed for WiMAX Point to Multi Point networks to provide quality of service to handover nodes . Moreover a delay reduction method is proposed to reduce the packet delivery delays during handover . A Call Admission Control algorithm is proposed to handle handover calls of various service classes fairly according to their priorities . A bandwidth borrowing scheme is proposed to reduce the handover call dropping probabilities of various service classes while not starving the ongoing calls of lower priority service classes . A Markov model is developed to analyze the proposed CAC method and to obtain the approximate handover call dropping probabilities of various service classes . Simulation experiments are conducted to establish the performance advantages of the proposed handover management and CAC methods . 


Summary: A distributed base station cooperation method for WiMAX PMP networks is proposed. A delay reduction method to support delay requirements during handover is proposed. A fair and starvation preventing call admission control method is proposed. Analytical dropping probabilities of various service classes are obtained.
Wireless sensor networks have a wide range of applications in our lifetime . Indeed WSNs perform a various missions and tasks in odor localization firefighting medical service surveillance and security . In order to accomplish these tasks the sensors have to perform partitioning protocols to form an organization into clusters and cliques . The hierarchical clustering is the key solution for WSNs to deal with the scalability problems in a network composed of millions of nodes . In this paper a new hierarchical partitioning scheme is presented named MCCC . It is cliques and clusters based hierarchical scheme that takes into account the size of cliques and clusters it also minimizes the number of hops between the cluster head and its nodes . The proposed scheme is motivated by the need to have minimum and maximum size for cliques and clusters . This hierarchical scheme is proposed to respond to the energy and memory constraints for WSNs . 


Summary: This paper presents a hierarchical scheme for sensor networks clustering. The solution proposed combines clustering into cliques and clusters. It takes into account the sizes of cliques and clusters for scalability and energy saving purposes. It improves the end to end delay compared to existing works.
With lot of hype surrounding policy based computing XACML has become the widely used de facto standard for managing access to open and distributed service based environments like Web services . However like any other policy language XACML has complex syntax which makes the policies specification process both time consuming and error prone especially with large size policies that govern complex systems . Moreover with the diversity of rules and conditions hidden conflicts redundancies and access flaws are more likely to arise which expose Web services to security breaches at runtime . This paper proposes a UML profile that allows systematic model driven specification of XACML policies to resolve the complexity of policies designation . Based on mathematical sets that explore the rules meanings the paper provides also a design level analysis to detect anomalies in the specified policies prior to their enforcement in the system . A real life case study demonstrates the feasibility and efficiency of the proposition . 


Summary: We provide UML profile for model driven specification of XACML policies. We propose a set based design level XACML policy analysis approach. We devise algorithms for design level detection of conflicts redundancies and flaws. We provide dynamic policies evaluation to control access to critical resources.
With the development of the Internet of Things more and more devices that have diverse computing and communication capabilities are interconnected . For multimedia multicast in the IoT a fixed flow rate can not meet the quality of service requirements of heterogeneous devices and each device may not get the information from all other devices . In order to satisfy the heterogeneous requirements we develop a distributed algorithm to solve the inter layer flow optimization problem based on network coding for multimedia multicast in the IoT by using primal decomposition and primal dual analysis . We also apply Lyapunov theory to prove the convergence and global stability of the proposed iterative algorithm . Numerical results demonstrate that the proposed algorithm has better flexibility stability and implementation advantages than the previous intra layer ones . 


Summary: A method to save bandwidth for multimedia multicast in IoT. A distributed algorithm of joint rate control and multicast layer flow allocation. The inter layer network coding is more stable than the intra layer ones.
The scale free topology is robust when confronted with random faults but it is fragile when confronted with selective remove attacks. In this paper we propose a new scale free topology model which has both fault tolerance against random faults and intrusion tolerance against selective remove attacks at the same time. Then the mathematical expression of the topological degree distribution is derived. Through analyzing the effect of topological degree distribution on these properties of topological fault tolerance and topological intrusion tolerance the optimal scale free topology which can keep the fault tolerance and maximize intrusion tolerance is obtained. We performed extensive experiments on the proposed model and compared it with other existing models. The simulation results show that the new scale free topology model can keep the character that the scale free topology has a stronger robustness to random faults. And it also can reduce their fragility for selective remove attacks and further prolong its lifetime. 


Summary: Two criterions of measuring the topological properties are discussed. Effect of degree distribution on topological properties is analyzed mathematically. The scale free topology model has adjustable scaling exponent is proposed. The proposed model is robustness to random faults and selective remove attacks.
This paper aims to develop a location based services supported Dr.What Info system i.e. a master multi agent system on what the information is using Google maps and an image recognition technology as a tourism information provider and as a route planner for tourists. Users can have great fun during vacation travels through an easy to use interface integrating smartphone GPS function a QR Bar code reader and easy access to a cloud database to find all of the required web services. In particular given an archeological site in New Taipei City Taiwan for testing purposes the presented system is demonstrated not only as a provider of information on popular tourist attractions but also as a high performance GPS navigation device to guide users toward their desired destinations. The complete system developments displays and corresponding experiments and comparisons show that the research results demonstrate performance superiority over a number of previous studies. 


Summary: A location based services and Google maps based Dr.What Info multi agent system is proposed. An archeologic site in New Taipei City Taiwan for testing purposes produces results regarding the completeness and feasibility of the proposed architecture. The deep and complete system developments displays and corresponding experiments and comparisons show that the research results demonstrate performance superiority over a number of previous studies.
Digital watermarking based biometric images protecting has been an active research focus . In this paper we propose a face image protection scheme based on semi fragile self recoverable watermarking . Authentication watermark is generated from the singular value decomposition coefficients for each image block and information watermark is generated from the principal component analysis coefficients . Both of them are embedded into wavelet medium frequency coefficients by using the proposed group based wavelet quantization method . On the authentication side after identifying the tampered regions the proposed method can recover the tampered face images by using the hidden information watermark . Experimental results demonstrate that the proposed watermark scheme has high localization accuracy and robustness comparing with the existing techniques and the recovered PCA coefficients can be used to reconstruct the Eigen face image or be directly used for recognition system . 


Summary: PCA coefficients are used to generate information watermarks and the authentication watermarks are generated using SVD coefficients. The embedding position is generated by a chaotic map and it improves the security of the proposed method. Watermarks are embedded into the mid frequency bands by using our proposed SDPQ method.
Representation and reasoning about context information is a main research area in Ambient Intelligence . Context modeling in such applications is facing openness and heterogeneity . To tackle such problems we argue that usage of semantic web technologies is a promising direction . We introduce CONSERT an approach for context meta modeling offering a consistent and uniform means for working with domain knowledge as well as constraints and meta properties thereof . We provide a formalization of the model and detail its innovative implementation using techniques from the semantic web community such as ontology modeling and SPARQL . A stepwise example of modeling a commonly encountered AmI scenario showcases the expressiveness of our approach . Finally the architecture of the representation and reasoning engine for CONSERT is presented and evaluated in terms of performance . 


Summary: We formally define an ontology based context meta model. Approach offers uniform modeling of context content annotation and dependencies. Reasoning cycle performs automatic computation of temporal continuity of situations. OSGi compatible reasoning engine exploits the context meta model in customizable way.
This paper analyzes the performance of spectrum sensing based energy detection in cognitive radio networks over generalized fading channels . The fading channel is modeled by the extended generalized K distribution . Exact and accurate analytical expressions for the average detection probability with different detection schemes such as single channel diversity reception and cooperative spectrum sensing are derived and evaluated . Obtained expressions can be reduced to other well known fading channels such as Weibull Nakagami m and Rayleigh . It is shown that the analytical framework can be used for both integer and non integer values of the fading shadowing parameters . The impact of key fading shadowing parameters on the performance of energy detectors is discussed with receiver operating characteristics curves . The accuracy of the derived analytical expressions is corroborated via Monte Carlo simulation results . 


Summary: Performance of ED in CRNs over EGK fading channels is analyzed. Unified analytical expressions for the average detection probability are derived. Performance of diversity reception and cooperative sensing is analyzed.
Data centers use dynamic virtual machine consolidation to reduce power consumption . Existing consolidation mechanisms are not efficient in cloud data centers which have heterogeneous hardware infrastructure huge scale and highly variable non stationary workloads . We use game theory to develop a novel distributed mechanism for both heterogeneous and homogeneous data centers of cloud computing . Our mathematical analysis shows that our mechanism converges after a finite number of migrations . In addition we show that our worst case power consumption is only 23 more than the theoretical minimum . In order to validate our claim we preform simulation in CloudSim with real workload traces from Google data centers . 


Summary: We propose a game for reducing power consumption in data centers. We show that the game always converges to a pure Nash equilibrium. We establish upper bounds on the convergence time of the game. We prove that PoA 1.23. We show that PoS 1.
This paper focuses on the spectrum sensing mechanisms which could improve network throughput through the sensing strategy optimization and cooperative spectrum sensing methods . In order to guarantee an integrated and effective research we take the whole channel scenarios into consideration i.e . Single Secondary user with Single and Multiple Channels Multiple Secondary users with Single and Multiple Channels . Moreover according to the specific feature of each scenario different sensing methods are adopted i.e . optimal sensing period to maximize network throughput for SSSC a novel sensing method to minimize searching time for SSMC partial cooperative spectrum sensing mechanism for MSMC and setting a spectrum pool in the fusion center to record the channel states for MSMC . Simulation results demonstrate that our methods can improve spectrum efficiency network throughput and channel utilization especially when the spectrum is utilized inadequately . 


Summary: Study spectrum sensing mechanisms by the optimization of network parameters and strategies. The proposed mechanisms have fewer sensing overhead. SUs can access into the spectrum holes efficiently and transmit with a low power.
Stealthy attackers move patiently through computer networks taking days weeks or months to accomplish their objectives in order to avoid detection . As networks scale up in size and speed monitoring for such attack attempts is increasingly a challenge . This paper presents an efficient monitoring technique for stealthy attacks . It investigates the feasibility of proposed method under number of different test cases and examines how design of the network affects the detection . A methodological way for tracing anonymous stealthy activities to their approximate sources is also presented . The Bayesian fusion along with traffic sampling is employed as a data reduction method . The proposed method has the ability to monitor stealthy activities using 10 20 size sampling rates without degrading the quality of detection . 


Summary: A scalable monitoring scheme for stealthy attacks on computer networks is presented. Bayesian fusion along with traffic sampling is used as a data reduction method. Stealthy activities can be detected using 10 20 size sampling rates. A tracing algorithm for anonymous stealthy activities to their sources is presented. The effect of network parameters on detection is investigated.
Advances in wireless ad hoc network techniques have spurred the development of new approaches to increase network efficiency . One of the more popular approaches is swarm intelligence . Swarm intelligence imitates the collective behavior of biological species to solve network routing problems . Meanwhile weakly connected dominating sets can serve as auxiliary structures for clustering nodes in the network . This paper uses the clustering concept of WCDS to propose an improved ant based on demand clustering routing protocol for wireless ad hoc networks . Network states information is obtained from the Forward Ant and is only broadcast by the head of every cluster thus decreasing the overhead required to transmit ant packets . To increase network efficiency the pseudo random proportional selection strategy is used to evaluate the best path from the source node to the destination node by the Backward Ant . 


Summary: We propose a novel WCDS assisted ACO based routing protocol. The on demand feature of AODV is used to improve the ACO scheme. WCDS works as an auxiliary structure to broadcast the Forward Ant message. A LCC strategy is used to maintain the WCDS architecture. The pseudo random proportional selection scheme selects the efficient route.
In cognitive wireless network throughput scheduling optimization under interference temperature constraints has attracted more attentions in recent years. A lot of works have been investigated on it with different scenarios. However these solutions have either high computational complexity or relatively poor performance. Throughput scheduling is a constraint optimization problem with NP Non deterministic Polynomial hard features. In this paper we proposed an immune clone based suboptimal algorithm to solve the problem. Suitable immune clone operators are designed such as encoding clone mutation and selection. The simulation results show that our proposed algorithm obtains near optimal performance and operates with much lower computational complexity. It is suitable for slowly varying spectral environments. 


Summary: A clone selection algorithm was proposed for the throughput problem in cognitive wireless network. The proposed algorithm presents a very reasonable tradeoff between computational complexity and performance. The proposed solution is more suitable for slowly varying spectral environments such as IEEE 802.22 networks in realistic network settings.
This article presents a new method for detecting objects on waters surfaces using colour elimination based on image erosion with a morphological variable . The proposed object detection method includes definitions of the target s colour space and colour deviation using Euclidean distance . It also introduces a procedure for image erosion using a morphological variable . In order to evaluate the proposed object detection method the experiments were performed on two different image databases the MSRA salient object database and a proprietary image database containing pictures of water activities typically encountered near hydro power plants . The experimental results show that the proposed object detection method enables efficient and robust detection of objects on of waters surfaces compared to other methods primarily based on the optimisation of image contrast and edge detection . 


Summary: A new method for detecting salient objects on waters surfaces is proposed. The proposed object detection method enables an efficient and robust detection. Four different methods were compared with the proposed CEIEMV method. The proposed method equals or outperforms the methods being compared.
With the development of the 6LoWPAN standard sensors can be natively integrated into the IP world becoming tiny information providers that are directly addressable by any Internet connected party . To protect the information gathered by sensors from any potential attacker on the Internet it is essential to have trustworthy real time information about the legitimacy of every attempt to interact with a sensor . Our approach to address this issue is Ladon a new security protocol specifically tailored to the characteristics of low capacity devices . In this paper we study the performance of Ladon showing that it successfully meets the requirements of the targeted environments . To that end we evaluate the delay and energy consumption of the execution of Ladon . The obtained results show that the cost of Ladon is bounded even in situations of high packet loss rates and comparable to that of other protocols that implement fewer security features . 


Summary: There is a lack of end to end security mechanisms tailored to IP enabled sensors. Ladon is proposed as end to end authentication and authorization protocol. We evaluate the time and energy cost of executing Ladon on the protected sensors. Ladon is proved to be a suitable security protocol for resource deprived devices.
The increasing costs of healthcare along with the increasing availability of new Personal Health Devices are the ingredients of the connected health vision . Also a growing number of consumer electronic and mobile devices are becoming capable of taking the role of a health gateway thus operating as a data collector for PHDs . In this context we present a system that enables PHDs to share information in home networks and with the Internet based on a new Internet of Things protocol namely the Constrained Application Protocol . CoAP is used along with the IEEE 11073 family of standards which is the main exchange data model for PHD communication . We discuss how the proposed system can be integrated to other connected health systems such as a Universal Plug and Play healthcare system . We detail how the CoAP communication model was adapted to the IEEE 11073 model . We also present a real PHD prototype and its evaluation results . These results demonstrated the feasibility of the proposed solution showing how its network overhead is around 50 lighter when compared to other protocols . Finally we tested the proposed solution based on different scenarios including a proof of concept integration with a service in the cloud using different wireless physical interfaces . 


Summary: We present a system that enables Personal Health Devices to share information in home networks and with the Internet. The Constrained Application Protocol CoAP is used as underlying protocol. The CoAP communication model was adapted to the ISO IEEE 11073 model in the proposed system. Results showed how the network overhead is around 50 lighter when compared to other protocols. Interoperability experiments demonstrated how the proposed solution can work with legacy systems.
This research proposes a framework for a real time implementation of a Brain Computer Interface . This interface is designed with a future objective of providing a testing platform as an interactive and intelligent Image Search and Retrieval tool that allows users disabled or otherwise to browse and search for images using non invasive electroencephalography signals . As a proof of concept a prototype system was designed and implemented to test real time data collection and navigation through the interface by detection and classification of event related potentials . A comparison of different feature extraction methods and classifiers for the detection of ERPs is performed on a standard data set to determine the best fit for the real time implementation of the BCI . The preliminary results of the real time implementation of the prototype BCI system are quantified by the success rate of the user subject in navigating through the interface and spelling a search keyword using the mental typewriter Hex O Speller . 


Summary: A framework for a real time implementation of a Brain Computer Interface. Implementation comparison of different feature extraction methods and classifiers. Accuracy processing time comparison for detection of event related potentials ERP. An implementation of a prototype system using the proposed BCI framework. Real time EEG data collection and classification of ERPs using Hex O Speller.
Automated recognition of brain tumors in magnetic resonance images is a difficult procedure owing to the variability and complexity of the location size shape and texture of these lesions . Because of intensity similarities between brain lesions and normal tissues some approaches make use of multi spectral anatomical MRI scans . However the time and cost restrictions for collecting multi spectral MRI scans and some other difficulties necessitate developing an approach that can detect tumor tissues using a single spectral anatomical MRI images . In this paper we present a fully automatic system which is able to detect slices that include tumor and to delineate the tumor area . The experimental results on single contrast mechanism demonstrate the efficacy of our proposed technique in successfully segmenting brain tumor tissues with high accuracy and low computational complexity . Moreover we include a study evaluating the efficacy of statistical features over Gabor wavelet features using several classifiers . This contribution fills the gap in the literature as is the first to compare these sets of features for tumor segmentation applications . 


Summary: A fully automatic system for detection of slices that contain tumor in MR images is presented. A fully automatic system for tumor segmentation using single spectral MR images is presented. A study for evaluating the efficacy of statistical features over Gabor wavelet features is included.