Investigation of Home Agent Load Balancing, Failure Detection and Recovery in IPv6 Network-based Mobility

Abstract

Mobile IPv6 came as an extensively acknowledged technology to support mobility in networks. Home agents are in charge for the registration of mobile devices and act as a key entity for the tunneling of data packets to the corresponding registered mobile nodes. A single home agent has administrative control over the critical tasks including home agent registration management, maintenance of cache data and tunneling of data packets to the mobile nodes that are away from their home networks and so on. However in this approach, home agent act as the sole failure point, which gave rise to the placement of multiple home agents to overcome this issue. The load balancing mechanism for multiple home agent deployment faces the problem of improper load sharing, signaling overhead and synchronization issues. Moreover, failure detection and recovery mechanism are inefficient in nature. It experiences a significant delay in tunneling of data packets and suffers from disconnection making it incompetent for the use in real time applications. Most of the existing methods for load sharing and failure detection use the concept of exchange of router advertisement message named as "heart beat messages" at a constant rate. The reduction in the interval of router advertisement can result in signaling overhead and synchronization issues. Hence, this paper investigates and analyzes the various load balancing mechanisms of mobile IPv6. In addition, it presents the comparative study of the failure detection and recovery mechanism of existing methods. Finally, it concludes that future work can be extended in the domain of distributed active load sharing mechanism and proactive failure detection.

Cluster Ensemble Method and Convolution Neural Network Model for Predicting Mental Illness

“…Though the psychological meaning of Twitter friends is perhaps less immediately apparent than the psychological sense of tweets, sometimes accounts users follow unknowingly disclose sensitive information about their psychological traits. Many people are living in a mobility environment [7]. Yan et al [3] aim to build a model using a convolution neural network (CNN) for the automatic detection and classification of personal mental illness at the workplace using the emotional labor and mental health questionnaire to help the staff assess their mental health on their own [4], [5].…”

Lane clearance approach for emergency vehicles in highways network

“…As a result, Fig 10 illustrates that the end-to-end latency falls with the increase in B W . Correspondingly, d pro was computed as 3.3 microseconds using Eq (5) in the wireless connection. The delay reduces with the increase in the B WL, as shown in Fig 11.…”

“…These methods are impaired by frequent network handoffs, resulting in more message communication due to the triangle routing of MIPv4 and service interruptions. To address this issue, the present paper proposes a unique approach for automatic lane clearance for an EV, using mobile IPv6 (MIPv6), the next generation internet protocol and wireless networks [5,6] to communicate the information an approaching EV to nearby vehicles. The key benefit of MIPv6 is adapted such that even though the EVs change their locations and IP addresses, the pre-existing connections are maintained.…”