We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

http://romisatriawahono.net/lecture/rm/survey/machine%20learning/Afsar%20-%20Clustering%20in%20Sensor%20Networks%20-%202014.pdf

Clustering in sensor networks

Safety-critical applications in cooperative vehicular networks require authentication of nodes and messages. Yet, privacy of individual vehicles and drivers must be maintained. Pseudonymity can satisfy both security and privacy requirements. Thus, a large body of work emerged in recent years, proposing pseudonym solutions tailored to vehicular networks. In this survey, we detail the challenges and requirements for such pseudonym mechanisms, propose an abstract pseudonym lifecycle, and give an extensive overview and categorization of the state of the art in this research area. Specifically, this survey covers pseudonym schemes based on public key and identity-based cryptography, group signatures and symmetric authentication. We compare the different approaches, give an overview of the current state of standardization, and identify open research challenges.

Pseudonym Schemes in Vehicular Networks: A Survey

Wireless sensor network (WSN) systems are typically composed of thousands of sensors that are powered by limited energy resources. To extend the networks longevity, clustering techniques have been introduced to enhance energy efficiency. This paper presents a survey on clustering over the last two decades. Existing protocols are analyzed from a quality of service (QoS) perspective including three common objectives, those of energy efficiency, reliable communication and latency awareness. This review reveals that QoS aware clustering demands more attention. Furthermore, there is a need to clarify how to improve quality of user experience (QoE) through clustering. Understanding the users’ requirements is critical in intelligent systems for the purpose of enabling the ability of supporting diverse scenarios. User awareness or user oriented design is one remaining challenging problem in clustering. In additional, this paper discusses the potential challenges of implementing clustering schemes to Internet of Things (IoT) systems in 5G networks. We indicate that clustering techniques enhanced with smart network selection solutions could highly benefit the QoS and QoE in IoT. As the current studies for WSNs are conducted either in homogeneous or low level heterogeneous networks, they are not ideal or even not able to function in highly dynamic IoT systems with a large range of user scenarios. Moreover, when 5G is finally realized, the problem will become more complex than that in traditional simplified WSNs. Several challenges related to applying clustering techniques to IoT in 5G environment are presented and discussed.

A Survey of Clustering Techniques in WSNs and Consideration of the Challenges of Applying Such to 5G IoT Scenarios

Book review: Applied cryptography: Protocols, algorithms, and source code in C

Most of the research in energy efficient data gathering in data centric applications of wireless sensor networks is motivated by LEACH scheme which by allowing rotation of clusterhead role among the sensor nodes tries to distribute the energy consumption over the network. Selection of sensor node for such role rotations greatly affects the energy efficiency of the network. Hence many proposals are reported in the literature suggesting different clusterhead selection strategies. However, till now, these strategies have not been talked about for their collective understanding. So, efforts have been made, in this paper, to discuss them in unison and present a global view. Comparison of their costs of clusterhead selection in different rounds and after effects like cluster formation, distribution of clusterheads and creation of clusters shows a need of more energy efficient clusterhead selection strategy.

Clusterhead selection in clustering algorithms for wireless sensor networks: A survey

Mobile ad hoc network is a collection of mobile nodes communicating through wireless channels without any existing network infrastructure or centralized administration. Because of the limited transmission range of wireless network interfaces, multiple \"hops\" may be needed to exchange data across the network. In order to facilitate communication within the network, a routing protocol is used to discover routes between nodes. The primary goal of such an ad hoc network routing protocol is correct and efficient route establishment between a pair of nodes so that messages may be delivered in a timely manner. Route construction should be done with a minimum of overhead and bandwidth consumption. This paper examines two routing protocols for mobile ad hoc networks– the Destination Sequenced Distance Vector (DSDV), the tabledriven protocol and the Ad hoc OnDemand Distance Vector routing (AODV), an On –Demand protocol and evaluates both protocols based on packet delivery fraction, normalized routing load, average delay and throughput while varying number of nodes, speed and pause time. Keywords—AODV, DSDV, MANET, relative performance

Performance Comparison and Analysis of Table-Driven and On-Demand Routing Protocols for Mobile Ad-hoc Networks

Security has always been a great concern whenever there is communication between sender and receiver. To overcome the issues of security breaches many cryptographic algorithms are used like: AES, DES, Triple DES, Blowfish, etc. The objective of this paper is to enhance and evaluate the Blowfish algorithm on the basis of different parameters like Encryption Quality, Correlation Coefficients, Key Sensitivity Test and Size of Output File. The ‘f’ function is modified by mixing the XOR and addition used in the original algorithm. Four cases are created and analyzed. The results of all the tests conducted on these cases lead to a common conclusion that the security of the modified algorithm with different cases makes the original Blowfish algorithm more compact and more secure than the earlier.

/pdf/analysis-of-modified-blowfish-algorithm-in-different-cases-44lddw51fl.pdf

Analysis of modified Blowfish algorithm in different cases with various parameters

An optimal intrusion detection system using recursive feature elimination and ensemble of classifiers

Clustering is an important research area for mobile ad hoc networks (MANETs) as it increases the capacity of network, reduces the routing overhead and makes the network more scalable in the presence of both high mobility and a large number of mobile nodes. In clustering the clusterhead manage and store recent routing information. However the frequent change of clusterhead leads to loss of routing information stored, changes the route between two nodes, affects the performance of the routing protocol and makes the cluster structure unstable. Communication overhead in terms of exchanging messages is needed to elect a new clusterhead. The goal then would be to keep the clusterhead change as least as possible to make cluster structure more stable, to prevent loss of routing information which in turn improve the performance of routing protocol based on clustering. This can be achieved by an efficient cluster maintenance scheme. In this work, a novel clustering algorithm, namely Incremental Maintenance Clustering Scheme (IMS) is proposed for Mobile Ad Hoc Networks. The goals are yielding low number of clusterhead and clustermember changes, maintaining stable clusters, minimizing the number of clustering overhead. Through simulations the performance of IMS is compared with that of least cluster change (LCC) and maintenance scheme of Cluster Based Routing Protocol (CBRP) in terms of the number of clusterhead changes, number of cluster-member changes and clustering overhead by varying mobility and speed. The simulation results demonstrate the superiority of IMS over LCC and maintenance scheme of CBRP. Index Terms—MANET, CBRP, cluster maintenance, control overhead, routing

Narendra Singh Yadav

Papers

Clusterhead selection in clustering algorithms for wireless sensor networks: A survey

Performance Comparison and Analysis of Table-Driven and On-Demand Routing Protocols for Mobile Ad-hoc Networks

Analysis of modified Blowfish algorithm in different cases with various parameters

An optimal intrusion detection system using recursive feature elimination and ensemble of classifiers

A Low Control Overhead Cluster Maintenance Scheme for Mobile Ad hoc NETworks (MANETs)