Published in 2026
G.Vidhya
In mobile opportunistic social networks (MOSNs), mobile devices carried by people communicate with each other directly when they meet for proximity-based MOSN services (e.g., file sharing) without the support of infrastructures. In current methods, when nodes meet, they simply communicate with their real IDs, which leads to privacy and security concerns. Anonymizing real IDs among neighbor nodes solves such concerns. However, this prevents nodes from collecting real ID-based encountering information, which is needed to support MOSN services. Therefore, in this paper, we propose FaceChange that can support both anonymizing real IDs among neighbor nodes and collecting real ID-based encountering information. For node anonymity, two encountering nodes communicate anonymously. Only when the two nodes disconnect with each other, each node forwards an encrypted encountering evidence to the encountered node to enable encountering information collection. A set of novel schemes are designed to ensure the confidentiality and uniqueness of encountering evidences. FaceChange also supports fine grained control over what information is shared with the encountered node based on attribute similarity (i.e., trust), which is calculated without disclosing attributes. Advanced extensions for sharing real IDs between mutually trusted nodes and more efficient encountering evidence collection are also proposed. Extensive analysis and experiments show the effectiveness of Face Change on protecting node privacy and meanwhile supporting the encountering information collection in MOSNs. Implementation on smartphones also demonstrates its energy efficiency.
Mobile Opportunistic Social Networks, peer-to-peer, Trust Authority, FaceChange
M.Buvaneswari, P.Saranya
Distributed Computing is a field of computer science that studies distributed systems. A distributed system is a model in which components located on networked computers communicate and coordinate their actions by passing messages. The components interact with each other in order to achieve a common goal. Efficient file query is important to the overall performance of peer-to-peer (P2P) file sharing systems. Clustering peers by their common interests can significantly enhance the efficiency of file query. Clustering peers by their physical proximity can also improve file query performance. However, few current works are able to cluster peers based on both peer interest and physical proximity. Although structured P2Ps provide higher file query efficiency than unstructured P2Ps, it is difficult to realize it due to their strictly defined topologies. In this work, we introduce a Proximity-Aware and Interest-clustered P2P file sharing System (PAIS) based on a structured P2P, which forms physically-close nodes into a cluster and further groups physically-close and common-interest nodes into a sub-cluster based on a hierarchical topology. PAIS uses an intelligent file replication algorithm to further enhance file query efficiency.
Distributed Computing, peer-to-peer, Distributed Hash Tables
M.Buvaneswari, P.Saranya
Mobile ad hoc network (MANET) is a collection of wireless mobile nodes that dynamically form a temporary network without the reliance of any infrastructure or central administration. Energy consumption is considered as one of the major limitations in MANET, as the mobile nodes do not possess permanent power supply and have to rely on batteries, thus reducing network lifetime as batteries get exhausted very quickly as nodes move and change their positions rapidly across MANET. This paper highlights the energy consumption in MANET by applying the fitness function technique to optimize the energy consumption in ad hoc on demand multipath distance vector (FF-AOMDV) routing protocol. The proposed protocol is called FF-AOMDV with the fitness function (FF-FFAOMDV).
Mobile ad hoc network, fitness function, Internet Protocol
K.Vimala, R.Deepika
Our study the capability of localizing node failures in communication networks from binary states (normal/failed) of end-to-end paths. Given a set of nodes of interest, uniquely localizing failures within this set requires that different observable path states associate with different node failure events. However, this condition is difficult to test on large networks due to the need to enumerate all possible node failures. Our first contribution is a set of sufficient/necessary conditions for identifying a bounded number of failures within an arbitrary node set that can be tested in polynomial time. In addition to network topology and locations of monitors, our conditions also incorporate constraints imposed by the probing mechanism used. We consider three probing mechanisms that differ according to whether measurement paths are: (i) arbitrarily controllable; (ii) controllable but cycle-free; or (iii) uncontrollable (determined by the default routing protocol). Our second contribution is to quantify the capability of failure localization through: 1) the maximum number of failures (anywhere in the network) such that failures within a given node set can be uniquely localized and 2) the largest node set within which failures can be uniquely localized under a given bound on the total number of failures.
Computer Network ,Communication,Topology,LAN,WiFi
K.Vimala, M.Sathya
In mobile opportunistic social networks (MOSNs), mobile devices carried by people communicate with each other directly when they meet for proximity-based MOSN services (e.g., file sharing) without the support of infrastructures. In current methods, when nodes meet, they simply communicate with their real IDs, which leads to privacy and security concerns. Only when the two nodes disconnect with each other, each node forwards an encrypted encountering evidence to the encountered node to enable encountering information collection. A set of novel schemes are designed to ensure the confidentiality and uniqueness of encountering evidences. FaceChange also supports fine grained control over what information is shared with the encountered node based on attribute similarity (i.e., trust), which is calculated without disclosing attributes. Advanced extensions for sharing real IDs between mutually trusted nodes and more efficient encountering evidence collection are also proposed.
Mobile Opportunistic Social Networks, Relaying Scheme, peer-to-peer
J.Kasthuri, R.Bharathi
With the constantly increasing number of new electronic devices capable of capturing, editing, storing and sharing video content all over the world, the volume of video data being transmitted in today’s communication networks is significantly growing. In typical video transmission systems, video signals are compressed and sent to the decoder through an error-prone communication channel that may corrupt the compressed video signal, causing the degradation of the final decoded video quality. This paper studied about Perceptually Driven Error Protection (PDEP) video codec provides a good alternative to traditional error protection video coding schemes, notably FEC-based schemes, even when the perceptual aspects of the video content are not considered.
Wireless Sensor Network, Perceptually Driven Error Protection, Video Compression, Multimedia Streams
K.Amsaveni, P.Balamurugan
Cloud computing utilizes with an attractive tag line ‘pay-as-you-use’ for attracting users to its great elasticity and scalability of resources at relatively low cost. The authority of the cloud computing is considered with respect to its technological transformations and business benefits, the future enterprise applications are completely dependent on it. It has its individual benefits; nowadays cryptography is more useful than encryption and decryption. Authentication is a basic part of our daily life as the privacy protection. We use authentication throughout to process day-to-day lives when we sign our name to some document, where our agreements and decisions are communicated electronically for providing authentication. In this paper discussed Asymmetric or public-key encryption algorithms like Diffie-Hellman, RSA, ECDH, ECC, ECDSA etc.
Cloud Computing, Security Algorithms, Elliptic curve cryptography,ECDH,ECDSA
E.Prabakar Raj, R.Senthilkumar
In recent years several approaches to knowledge discovery and data mining, and in particular to clustering, have been developed, but only a few of them are designed for distributed data sources. Distributed clustering model most closely related to statistics is based on distribution models. Clusters can then easily be defined as objects belonging most likely to the same distribution. A nice property of this approach is that this closely resembles the way artificial data sets are generated: by sampling random objects from a distribution. The aim of this paper is to explain Distributed Data Mining (DDM) started to gain attention during the late nineties. Al though it is still a young area of research, the body of literature on DDM constitutes a sizeable portion of the broader data mining literature. DDM in general deals with the problem of finding patterns in an environment where data is either naturally distributed, or could be artificially partitioned across computing nodes. It implies distribution of one or more of: users, data, hardware, or mining software. Centralized data mining systems do not address some the requirements of distributed environments, such as scalability and cooperation. Data mining in distributed environments is known as Distributed Data Mining (DDM), and sometimes as Distributed Knowledge Discovery (DKD).
Data Mining, Distributed, Density models, Subspace models, Group models, k-means