Smart detection and prevention procedure for DoS attack in MANET

A self-organized wireless communication short-lived network containing collection of mobile nodes is mobile ad hoc network (MANET). The mobile nodes communicate with each other by wireless radio links without the use of any pre-established fixed communication network infrastructure or centralized administration, such as base stations or access points, and with no human intervention. In addition, this network has potential applications in conference, disaster relief, and battlefield scenario, and have received important attention in current years. There is some security concern that increases fear of attacks on the mobile ad-hoc network. The mobility of the NODE in a MANET poses many security problems and vulnerable to different types of security attacks than conventional wired and wireless networks. The causes of these issues are due to their open medium, dynamic network topology, absence of central administration, distributed cooperation, constrained capability, and lack of clear line of defense. Without proper security, mobile hosts are easily captured, compromised, and attacked by malicious nodes. Malicious nodes behavior may deliberately disrupt the network so that the whole network will be suffering from packet losses. One of the major concerns in mobile ad-hoc networks is a traffic DoS attack in which the traffic is choked by the malicious node which denied network services for the user. Mobile ad-hoc networks must have a safe path for transmission and correspondence which is a serious testing and indispensable issue. So as to provide secure communication and transmission, the scientist worked explicitly on the security issues in versatile impromptu organizations and many secure directing conventions and security measures within the networks were proposed. The goal of the work is to study DoS attacks and how it can be detected in the network. Existing methodologies for finding a malicious node that causes traffic jamming is based on node’s retains value. The proposed approach finds a malicious node using reliability value determined by the broadcast reliability packet (RL Packet). In this approach at the initial level, every node has zero reliability value, specific time slice, and transmission starts with a packet termed as reliability packet, node who responded properly in specific time, increases its reliability value and those nodes who do not respond in a specific time decreases their reliability value and if it goes to less than zero then announced that it’s a malicious node. Reliability approach makes service availability and retransmission time

Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey

Learning about appearance embedding is of great importance for a variety of different computer-vision applications, which has prompted a surge in person re-identification (Re-ID) papers. The aim of these papers has been to identify an individual over a set of non-overlapping cameras. Despite recent advances in RGB–RGB Re-ID approaches with deep-learning architectures, the approach fails to consistently work well when there are low resolutions in dark conditions. The introduction of different sensors (i.e., RGB–D and infrared (IR)) enables the capture of appearances even in dark conditions. Recently, a lot of research has been dedicated to addressing the issue of finding appearance embedding in dark conditions using different advanced camera sensors. In this paper, we give a comprehensive overview of existing Re-ID approaches that utilize the additional information from different sensor-based methods to address the constraints faced by RGB camera-based person Re-ID systems. Although there are a number of survey papers that consider either the RGB–RGB or Visible-IR scenarios, there are none that consider both RGB–D and RGB–IR. In this paper, we present a detailed taxonomy of the existing approaches along with the existing RGB–D and RGB–IR person Re-ID datasets. Then, we summarize the performance of state-of-the-art methods on several representative RGB–D and RGB–IR datasets. Finally, future directions and current issues are considered for improving the different sensor-based person Re-ID systems

Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey

Learning about appearance embedding is of great importance for a variety of different computer-vision applications, which has prompted a surge in person re-identification (Re-ID) papers. The aim of these papers has been to identify an individual over a set of non-overlapping cameras. Despite recent advances in RGB–RGB Re-ID approaches with deep-learning architectures, the approach fails to consistently work well when there are low resolutions in dark conditions. The introduction of different sensors (i.e., RGB–D and infrared (IR)) enables the capture of appearances even in dark conditions. Recently, a lot of research has been dedicated to addressing the issue of finding appearance embedding in dark conditions using different advanced camera sensors. In this paper, we give a comprehensive overview of existing Re-ID approaches that utilize the additional information from different sensor-based methods to address the constraints faced by RGB camera-based person Re-ID systems. Although there are a number of survey papers that consider either the RGB–RGB or Visible-IR scenarios, there are none that consider both RGB–D and RGB–IR. In this paper, we present a detailed taxonomy of the existing approaches along with the existing RGB–D and RGB–IR person Re-ID datasets. Then, we summarize the performance of state-of-the-art methods on several representative RGB–D and RGB–IR datasets. Finally, future directions and current issues are considered for improving the different sensor-based person Re-ID systems