Description:
When network resources are shared between Instantaneous Request (IR) and Book-Ahead (BA) connections, activation of future BA connections may cause preemption of on-going IR connections due to resource scarcity. Rerouting of preempted calls via alternative feasible paths is often considered as the final option to restore and maintain service continuity. Existing rerouting techniques, however, do not ensure acceptably low service disruption time and suffer from high failure rate and low network utilization. In this work, a new rerouting strategy is proposed that estimates the future resource scarcity, identifies the probable candidate connections for preemption and initiates the rerouting process in advance for those connections. Simulations on a widely used network topology suggest that the proposed rerouting scheme achieves a higher successful rerouting rate with lower service disruption time, while not compromising other network performance metrics like utilization and call blocking rate.
Description:
Malware replicates itself and produces offspring with the same characteristics but different signatures by using code obfuscation techniques. Current generation Anti-Virus (AV) engines employ a signature-template type detection approach where malware can easily evade existing signatures in the database. This reduces the capability of current AV engines in detecting malware. In this paper we propose a hybrid framework for malware detection by using the hybrids of Support Vector Machines Wrapper, Maximum-Relevance–Minimum-Redundancy Filter heuristics where Application Program Interface (API) call statistics are used as a malware features. The novelty of our hybrid framework is that it injects the filter’s ranking score in the wrapper selection process and combines the properties of both wrapper and filters and API call statistics which can detect malware based on the nature of infectious actions instead of signature. To the best of our knowledge, this kind of hybrid approach has not been explored yet in the literature in the context of feature selection and malware detection. Knowledge about the intrinsic characteristics of malicious activities is determined by the API call statistics which is injected as a filter score into the wrapper’s backward elimination process in order to find the most significant APIs. While using the most significant APIs in the wrapper classification on both obfuscated and benign types malware datasets, the results show that the proposed hybrid framework clearly surpasses the existing models including the independent filters and wrappers using only a very compact set of significant APIs. The performances of the proposed and existing models have further been compared using binary logistic regression. Various goodness of fit comparison criteria such as Chi Square, Akaike’s Information Criterion (AIC) and Receiver Operating Characteristic Curve ROC are deployed to identify the best performing models. Experimental outcomes based on the above criteria also show that the proposed hybrid framework outperforms other existing models of signature types including independent wrapper and filter approaches to identify malware.
Description:
One of the challenging aspects for wireless sensor network (WSN) clustering is to estimate the optimal cluster number. Most WSN clustering techniques have used an a priori value to determine the cluster number. A few techniques, available at the literature, attempted to find the optimal cluster number by energy minimization. However, due to the assumption of lossless communication, these initiatives do not always converge to the minimum energy as wireless channels are error-prone. As wireless communication is always vulnerable to the signal interference, environmental noise, and so on, it suffers from data loss. To capture the characteristics of the communication phenomenon in a real context and have better reliability, we need to embed the link quality into the cluster number determination process. To address this issue, in this paper together with the energy, we incorporate link quality metric into the theoretical underpinning to achieve an optimal cluster number analytically and finally justified the result by simulation.
Description:
This paper presents a canonical dual approach for solving nonconvex minimization problems in Euclidean distance geometry. The variant of this problem arises extensively in engineering and science, including computational biology, sensor network communications, database analysis, information technology, and global optimization. Due to the nonconvexity, most of these problems are NP-hard and traditional convex optimization methods can not be used directly for finding global optimal solutions. We first show that this type of nonconvex problems can be transferred to a concave maximization problem over a convex set. Then a general analytical solution is proposed by using the canonical duality theory. Applications are illustrated by network localization and minimization of Rosenbrock function. Furthermore, by using a perturbed canonical dual approach, a class of Euclidean distance problems can be converted to a unified concave maximization dual problem with zero duality gap, which can be solved by well-developed convex minimization methods.
Description:
The wireless networked sensors embedded with everyday objects will become an integral part of Future Internet, where the interaction among people, computer and those objects will shift the current Internet to a new paradigm, namely the Internet of Things. The terabyte torrent of data generated by billions of sensors belonging to a large number of distributed heterogeneous sensor networks in Future Internet will only be valuable if they can be effectively used on purpose, which leads to the necessity of an Internet scale query processing framework. In this paper, firstly, we focus on the distinct challenges present in Internet scale query processing over distributed sensor networks. Then, we propose a flexible and scalable system architecture capable of handling the complex scenario that might arise from the integration of a large number of such networks in Future Internet. Finally, we discuss the overall query processing methodology over such system and present some directions on the possible solutions to a number of identified research challenges. The outcome of this paper would foster the sensor network research in Future Internet domain
Description:
Malware replicates itself and produces offspring with the same characteristics but different signatures by using code obfuscation techniques. Current generation Anti-Virus (AV) engines employ a signature-template type detection approach where malware can easily evade existing signatures in the database. This reduces the capability of current AV engines in detecting malware. In this paper we propose a hybrid framework for malware detection by using the hybrids of Support Vector Machines Wrapper, Maximum-Relevance–Minimum-Redundancy Filter heuristics where Application Program Interface (API) call statistics are used as a malware features. The novelty of our hybrid framework is that it injects the filter’s ranking score in the wrapper selection process and combines the properties of both wrapper and filters and API call statistics which can detect malware based on the nature of infectious actions instead of signature. To the best of our knowledge, this kind of hybrid approach has not been explored yet in the literature in the context of feature selection and malware detection. Knowledge about the intrinsic characteristics of malicious activities is determined by the API call statistics which is injected as a filter score into the wrapper’s backward elimination process in order to find the most significant APIs. While using the most significant APIs in the wrapper classification on both obfuscated and benign types malware datasets, the results show that the proposed hybrid framework clearly surpasses the existing models including the independent filters and wrappers using only a very compact set of significant APIs. The performances of the proposed and existing models have further been compared using binary logistic regression. Various goodness of fit comparison criteria such as Chi Square, Akaike’s Information Criterion (AIC) and Receiver Operating Characteristic Curve ROC are deployed to identify the best performing models. Experimental outcomes based on the above criteria also show that the proposed hybrid framework outperforms other existing models of signature types including independent wrapper and filter approaches to identify malware.
Description:
Lateral Plains and the University of Ballarat’s Centre for eCommerce and Communications were commissioned by Yarra Ranges Council to undertake research to extend understanding of how better information communication technology (ICT) infrastructure and its use can lead to greater levels of innovation across the municipality. The online survey was completed between the period- March, 2011- April, 2011 275 valid responses were received. An ICT survey in online and hardcopy formats was used to gather an evidence base to support ICT. Summary available online. Qualitatitve data may be available by contacting CeCC.
Description:
A combination of qualitative and quantitative research methods were utilised to collect information from across the Great South Coast(GSC) region Victoria which included (5 municipalities:- Warrnambool City and the Shires of Corangamite, Glenelg, Moyne and Southern Grampians) and were aimed at information regarding telecommunications and broadband access and services, barriers and usage at local levels. Data collection methods included key stakeholder interviews, the online survey, case studies and spatial mapping of the responses and feedback garnered mainly from the surveys. Anticipated NBN access infrastructure has also been mapped.The adopted consultation and research methodology was designed to assess demand and support from business operators, local residents and other stakeholders for next generation broadband for the GSC region. The online survey was a major instrument for gathering data in the period to July 2011. The largest contributions to the 598 valid responses came from Warrnambool (n=166), Hamilton (n=94), Camperdown (n=29) and Portland (n=23). Summary available online. Qualitative data may be available by contacting CeCC.
Description:
A combination of qualitative and quantitative research methods were utilised to collect information from across the Great South Coast(GSC) region Victoria which included (5 municipalities:- Warrnambool City and the Shires of Corangamite, Glenelg, Moyne and Southern Grampians) and were aimed at information regarding telecommunications and broadband access and services, barriers and usage at local levels. Data collection methods included key stakeholder interviews, the online survey, case studies and spatial mapping of the responses and feedback garnered mainly from the surveys. Anticipated NBN access infrastructure has also been mapped.The adopted consultation and research methodology was designed to assess demand and support from business operators, local residents and other stakeholders for next generation broadband for the GSC region. The online survey was a major instrument for gathering data in the period to July 2011. The largest contributions to the 598 valid responses came from Warrnambool (n=166), Hamilton (n=94), Camperdown (n=29) and Portland (n=23). Summary available online. Qualitative data may be available by contacting CeCC.
Description:
Multimedia sensor services and technologies play an important role in seamlessly providing andmanaging health, sports, and other services to anyone, everywhere, and anytime. Media sensors are usually equipped with cameras, microphones, and other devices that produce media content and services. Such services and technologies enable caregivers and related professionals to have immediate access to required information for efficient decision making. Since media sensing technology development is growing, many research opportunities are emerging in a broad spectrum of application domains.
Description:
Multimedia sensor services and technologies play an important role in seamlessly providing andmanaging health, sports, and other services to anyone, everywhere, and anytime. Media sensors are usually equipped with cameras, microphones, and other devices that produce media content and services. Such services and technologies enable caregivers and related professionals to have immediate access to required information for efficient decision making. Since media sensing technology development is growing, many research opportunities are emerging in a broad spectrum of application domains.
Description:
WSNs generates a large amount of data in the form of stream and mining knowledge from the stream of data can be extremely useful. Association rules mining, from the sensor data, has been studied in recent literature. However, sensor association rules mining often produces a huge number of rules, but most of them either are redundant or fail to reflect the true correlation relationship among data objects. In this paper, we address this problem and propose mining of a new type of sensor behavioral pattern called associated-correlated sensor patterns. The proposed behavioral patterns capture not only association-like co-occurrences but also the substantial temporal correlations implied by such co-occurrences in the sensor data. Here, we also use a prefix tree-based structure called associated-correlated sensor pattern-tree (ACSP-tree), which facilitates frequent pattern (FP) growth-based mining technique to generate all associated-correlated patterns from WSN data with only one scan over the sensor database. Extensive performance study shows that our approach is time and memory efficient in finding associated-correlated patterns than the existing most efficient algorithms.
Description:
Mining interesting and useful knowledge from the huge amount of data gathered in wireless sensor networks is a challenging task. Works reported in literature use support metric-based sensor association rule which employs the occurrence frequency of patterns as criteria. Such criteria may not be appropriate for finding significant patterns. Moreover, temporal regularity in occurrence behavior should be considered as another important measure for assessing the importance of patterns in WSNs. Frequent sensor patterns that occur after regular intervals is called regularly frequent sensor patterns. Even though mining regularly frequent sensor patterns from sensor data stream is extremely important in many real-time applications, no such algorithm has been proposed yet. In this paper, we propose a novel tree structure called Regularly Frequent Sensor Pattern-tree (RSP-tree) and an efficient mining approach for finding regularly frequent sensor patterns from WSNs. Extensive performance analyses show that our technique is time and memory efficient in finding regularly frequent sensor patterns.
Description:
Spectrum allocation scheme in cognitive radio networks (CRNs) becomes complex when multiple CR users concomitantly need to be allocated new and suitable bands once the primary user returns. Most existing schemes focus on the gain of individual users, ignoring the effect of an allocation on other users and rely on the 'periodic sensing and transmission' cycle which reduces spectrum utilization. This paper introduces a scheme that exploits collaboration among users to detect PU's return which relieves active CR users from the sensing task, and thereby improves spectrum utilization. It defines a Capacity of Service (CoS) metric based on the optimal sensing parameters which measures the suitability of a band for each contending user and takes into consideration the impact of allocating a particular band on other band seeking users. The proposed scheme significantly improves capacity of service, reduces interference loss and collision, and hence, enhances dynamic spectrum access capabilities. (C) 2015 Elsevier Ltd. All rights reserved.