Key Management Building Blocks for Wireless Sensor Networks

Cryptography is the means to ensure data confidentiality, integrity and authentication in wireless sensor networks (WSNs). To use cryptography effectively however, the cryptographic keys need to be managed properly. First of all, the necessary keys need to be distributed to the nodes before the nodes are deployed in the field, in such a way that any two or more nodes that need to communicate securely can establish a session key. Then, the session keys need to be refreshed from time to time to prevent birthday attacks. Finally, in case any of the nodes is found to be compromised, the key ring of the compromised node needs to be revoked and some or all of the compromised keys might need to be replaced. These processes, together with the policies and techniques needed to support them, are called key management. The facts that WSNs (1) are generally not tamper-resistant; (2) operate unattended; (3) communicate in an open medium; (4) have no fixed infrastructure and pre-configured topology; (5) have severe hardware and resource constraints, present unique challenges to key management. In this article, we explore techniques for meeting these challenges. What distinguishes our approach from a routine literature survey is that, instead of comparing various known schemes, we set out to identify the basic cryptographic principles, or building blocks that will allow practitioners to set up their own key management framework using these building blocks

KALwEN: A New Practical and Interoperable Key Management Scheme for Body Sensor Networks

Key management is the pillar of a security architecture. Body sensor networks(BSNs) pose several challenges -- some inherited from wireless sensor networks(WSNs), some unique to themselves -- that require a new key management scheme to be tailor-made. The challenge is taken on, and the result is KALwEN, a new lightweight scheme that combines the best-suited cryptographic techniques in a seamless framework. KALwEN is user-friendly in the sense that it requires no expert knowledge of a user, and instead only requires a user to follow a simple set of instructions when bootstrapping or extending a network. One of KALwEN's key features is that it allows sensor devices from different manufacturers, which expectedly do not have any pre-shared secret, to establish secure communications with each other. KALwEN is decentralized, such that it does not rely on the availability of a local processing unit (LPU). KALwEN supports global broadcast, local broadcast and neighbor-to-neighbor unicast, while preserving past key secrecry and future key secrecy. The fact that the cryptographic protocols of KALwEN have been formally verified also makes a convincing case

Subtle hand gesture identification for HCI using temporal decorrelation source separation BSS of surface EMG

Hand gesture identification has various human computer interaction (HCI) applications. This paper presents a method for subtle hand gesture identification from sEMG of the forearm by decomposing the signal into components originating from different muscles. The processing requires the decomposition of the surface EMG by temporal decorrelation source separation (TDSEP) based blind source separation technique. Pattern classification of the separated signal is performed in the second step with a back propagation neural network. The focus of this work is to establish a simple, yet robust system that can be used to identify subtle complex hand actions and gestures for control of prosthesis and other HCI based devices. The proposed model based approach is able to overcome the ambiguity problems (order and magnitude problem) of BSS methods by selecting an a priori mixing matrix based on known hand muscle anatomy. The paper reports experimental results, where the system was able to reliably recognize different subtle hand gesture with an overall accuracy of 97%. The advantage of such a system is that it is easy to train by a lay user, and can easily be implemented in real time after the initial training. The paper also highlights the importance of mixing matrix analysis in BSS technique

Density Estimation Using a Generalized Neuron

Neural networks have been shown to be useful tools for density estimation. However, the training of neural network structures is time consuming and requires fast processors for practical applications. A new method with a generalized neuron (GN) for density estimation is presented in this paper. The GN is trained with the particle swarm optimization algorithm which is known to have fast convergence than the standard backpropagation algorithm. Results are presented to show that the GN can estimate the density functions for distribution functions with different means and variances. This density estimation method can also be applied to the multi-sensor data fusion proces

Necessary and sufficient conditions for optimal flow control in multirate multicast networks

The authors consider the optimal flow control problem in multirate multicast networks where all receivers of the same multicast group can receive service at different rates with different QoS. The objective is to achieve the fairness transmission rates that maximise the total receiver utility under the capacity constraint of links. They first propose necessary and sufficient conditions for the optimal solution to the problem, and then derive a new optimal flow control strategy using the Lagrangian multiplier method. Like the unicast case, the basic algorithm consists of a link algorithm to update the link price, and a receiver algorithm to adapt the transmission rate according to the link prices along its path. In particular if some groups contain only one receiver and become unicast, the algorithm will degrade to their previously proposed unicast algorithm

Limitations of ICA for artefact removal

This paper reports analysis of the limitations of using independent component analysis (ICA) for biosignal analysis especially artefact removal. The possible difficulty is that there are limited number of electrodes (recordings) making it an overcomplete problem (non-square ICA). The other difficulty is the distribution of biosignal being close to Gaussian. These two properties of the signals may make these outside the standard ICA application. This paper reports that ICA is able to successfully separate the biosignals if the number of recordings are not less than the number of sources. If that is not the case, ICA separates artefact component only when the corresponding artefact is predominant. The experiments demonstrate that the results are not reliable and hence the authors recommend that caution should be exercised before using ICA for such application

Holding and spawning of the edible oyster Crassostrea madrasensis during off season

Production of seed from the hatchery throughout the year would help to undertake stocking in the grow out systems at appropriate time which may vary in different areas. Ripe oysters with a size range of 70-120 mm were collected from Korampallam creek near Tuticorin. Thus holding the ripe oysters is a promising line of work for getting spawn in the hatchery outside the spawning period, leading to seed production throughout the year

A hierarchically combined classifier for license plate recognition

High accuracy and fast recognition speed are two requirements for real-time and automatic license plate recognition system. In this paper, we propose a hierarchically combined classifier based on an Inductive Learning Based Method and an SVM-based classification. This approach employs the inductive learning based method to roughly divide all classes into smaller groups. Then the SVM method is used for character classification in individual groups. Both start from a collection of samples of characters from license plates. After a training process using some known samples in advance, the inductive learning rules are extracted for rough classification and the parameters used for SVM-based classification are obtained. Then, a classification tree is constructed for further fast training and testing processes for SVMbased classification. Experimental results for the proposed approach are given. From the experimental results, we can make the conclusion that the hierarchically combined classifier is better than either the inductive learning based classification or the SVMbased classification in terms of error rates and processing speeds. © 2008 IEEE

Microalgal species as feed for conditioning adult oyster Crassostrea madrasensis (Preston)

The rate of removal of different microalgal cells in suspension at specific time interval in respect of six species differing in sizes such as Tetraselmis sp, Cheatoceros sp, Chlorella sp, Dicrataria sp., Isochrys sp, Chromulina sp, by Crassostrea madrasensis has been studied. The study revealed that often exhibit a signifiant degree of selectivity in the rate of filtration of certain algae. Further it is recorded that the filtration rate is not uniform throughout the experimental period of 24 hours. Oysters showed periods of high filtering activity and periods of relative quiescence

Segmentation of characters on car license plates

License plate recognition usually contains three steps, namely license plate detection/localization, character segmentation and character recognition. When reading characters on a license plate one by one after license plate detection step, it is crucial to accurately segment the characters. The segmentation step may be affected by many factors such as license plate boundaries (frames). The recognition accuracy will be significantly reduced if the characters are not properly segmented. This paper presents an efficient algorithm for character segmentation on a license plate. The algorithm follows the step that detects the license plates using an AdaBoost algorithm. It is based on an efficient and accurate skew and slant correction of license plates, and works together with boundary (frame) removal of license plates. The algorithm is efficient and can be applied in real-time applications. The experiments are performed to show the accuracy of segmentation. © 2008 IEEE