The deployment of small cells has been identified as one of the future-proof solutions to cope with the increasing demand for higher data rate and ubiquitous access in mobile networks. However, providing a reliable and cost-effective backhaul connectivity for small cells has become a key challenge. It is identified that the cost efficiency of small-cell…
Omnet++ Simulator
Implementation of network intrusion detection system using variant of decision tree algorithm
As the need of internet is increasing day by day, the significance of security is also increasing. The enormous usage of internet has greatly affected the security of the system. Hackers do monitor the system minutely or keenly, therefore the security of the network is under observation. A conventional intrusion detection technology indicates more limitation…
Detecting packet dropping nodes using machine learning techniques in Mobile ad-hoc network: A survey
Mobile ad-hoc networks have to suffer with different types of packet dropping attacks. Therefore, we need strong mechanism to detect these malevolent nodes and to classify normal and abnormal nodes as per the behavior of nodes. Machine learning techniques distinguish outlier nodes quickly and accurately provide classification by observing behavior of those nodes in the…
A novel performability assessment approach of Mobile Ad Hoc Network
In Mobile Ad hoc Networks (MANETs), system performance degrades when some switching nodes and/or communication links fail to operate. As a result, the performability (performance reliability) assessment of MANETs is very important. However, because of the impact of dynamic topology, the traditional system performability analysis methods, such as Dynamic Fault Tree (DFT) and Dynamic Reliability…
Transmission network expansion planning considering uncertainties in loads and renewable energy resources
This paper proposes a novel method for transmission network expansion planning (TNEP) that take into account uncertainties in loads and renewable energy resources. The goal of TNEP is to minimize the expansion cost of candidate lines without any load curtailment. A robust linear optimization algorithm is adopted to minimize the load curtailment with uncertainties considered…
A Comparative Performance Analysis of Interweave and Underlay Multi-Antenna Cognitive Radio Networks
In this paper, an analytical performance study for multi-antenna Cognitive Radio (CR) systems is presented. The two most popular CR approaches, namely, the interweave and underlay system designs, are considered and based on the derived analytical framework, a throughput-based comparison of these two system designs is presented. The system parameters are selected such that a…
Mobile Sink based Adaptive Immune Energy-Efficient Clustering Protocol for Improving the Lifetime and Stability Period of Wireless Sensor Networks
Energy hole problem is a critical issue for data gathering in Wireless Sensor Networks. Sensors near the static sink act as relays for far sensor and thus will deplete their energy very quickly, resulting energy holes in the sensor field. Exploiting the mobility of a sink has been widely accepted as an efficient way to…
Self-Diagnosis Technique for Virtual Private Networks Combining Bayesian Networks and Case-Based Reasoning
Fault diagnosis is a critical task for operators in the context of e-TOM (enhanced Telecom Operations Map) assurance process. Its purpose is to reduce network maintenance costs and to improve availability, reliability and performance of network services. Although necessary, this operation is complex and requires significant involvement of human expertise. The study of the fundamental…
The t/k-Diagnosability of Star Graph Networks
The t/k-diagnosis is a diagnostic strategy at system level that can significantly enhance the system’s self-diagnosing capability. It can detect up to t faulty processors (or nodes, units) which might include at most k misdiagnosed processors, where k is typically a small number. Somani and Peleg ([26], 1996) claimed that an n-dimensional Star Graph (denoted…
Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks
Automatic localization of the standard plane containing complicated anatomical structures in ultrasound (US) videos remains a challenging problem. In this paper, we present a learning based approach to locate the fetal abdominal standard plane (FASP) in US videos by constructing a domain transferred deep convolutional neural network (CNN). Compared with previous works based on low-level…