A Survey of Access Control Models in Wireless Sensor Networks

Copyright 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/)Wireless sensor networks (WSNs) have attracted considerable interest in the research community, because of their wide range of applications. However, due to the distributed nature of WSNs and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their proper functioning. Resource constraints in sensor nodes mean that security mechanisms with a large overhead of computation and communication are impractical to use in WSNs; security in sensor networks is, therefore, a challenge. Access control is a critical security service that offers the appropriate access privileges to legitimate users and prevents illegitimate users from unauthorized access. However, access control has not received much attention in the context of WSNs. This paper provides an overview of security threats and attacks, outlines the security requirements and presents a state-of-the-art survey on access control models, including a comparison and evaluation based on their characteristics in WSNs. Potential challenging issues for access control schemes in WSNs are also discussed.Peer reviewe

A User-Focused Reference Model for Wireless Systems Beyond 3G

This whitepaper describes a proposal from Working Group 1, the Human Perspective of the Wireless World, for a user-focused reference model for systems beyond 3G. The general structure of the proposed model involves two "planes": the Value Plane and the Capability Plane. The characteristics of these planes are discussed in detail and an example application of the model to a specific scenario for the wireless world is provided

The Future of the Internet III

Presents survey results on technology experts' predictions on the Internet's social, political, and economic impact as of 2020, including its effects on integrity and tolerance, intellectual property law, and the division between personal and work lives

BAN-GZKP: Optimal Zero Knowledge Proof based Scheme for Wireless Body Area Networks

BANZKP is the best to date Zero Knowledge Proof (ZKP) based secure lightweight and energy efficient authentication scheme designed for Wireless Area Network (WBAN). It is vulnerable to several security attacks such as the replay attack, Distributed Denial-of-Service (DDoS) attacks at sink and redundancy information crack. However, BANZKP needs an end-to-end authentication which is not compliant with the human body postural mobility. We propose a new scheme BAN-GZKP. Our scheme improves both the security and postural mobility resilience of BANZKP. Moreover, BAN-GZKP uses only a three-phase authentication which is optimal in the class of ZKP protocols. To fix the security vulnerabilities of BANZKP, BAN-GZKP uses a novel random key allocation and a Hop-by-Hop authentication definition. We further prove the reliability of our scheme to various attacks including those to which BANZKP is vulnerable. Furthermore, via extensive simulations we prove that our scheme, BAN-GZKP, outperforms BANZKP in terms of reliability to human body postural mobility for various network parameters (end-to-end delay, number of packets exchanged in the network, number of transmissions). We compared both schemes using representative convergecast strategies with various transmission rates and human postural mobility. Finally, it is important to mention that BAN-GZKP has no additional cost compared to BANZKP in terms memory, computational complexity or energy consumption

RF Localization in Indoor Environment

In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained