Lightweight authentication for recovery in wireless sensor networks

Wireless sensor networks (WSNs) suffer from a wide range of security attacks due to their limited processing and energy capabilities. Their use in numerous mission critical applications, however, requires that fast recovery from such attacks be achieved. Much research has been completed on detection of security attacks, while very little attention has been paid to recovery from an attack. In this paper, we propose a novel, lightweight authentication protocol that can secure network and node recovery operations such as re-clustering and reprogramming. Our protocol is based on hash functions and we compare the performance of two well-known lightweight hash functions, SHA-1 and Rabin. We demonstrate that our authentication protocol can be implemented efficiently on a sensor network test-bed with TelosB motes. Further, our experimental results show that our protocol is efficient both in terms of computational overhead and execution times which makes it suitable for low resourced sensor devices.

Network resilience in low-resource mobile wireless sensor networks

Wireless sensor networks (WSNs) are deployed in numerous mission critical applications in which the network needs to remain active for as long as possible while delivering quality information to a base station. However, WSNs suffer from a wide range of attacks due to their limited processing and energy capabilities. Their resiliency, however, depends on fast recovery from such attacks being achieved. In recent work, the authors developed and implemented clustering, reprogramming and authentication protocols involved in recovering stationary WSNs with low resources. In this paper, we determine the additional resources required in implementing these protocols in a mobile WSN.

We present recovery protocols on TinyOS motes for a low-resourced, mobile deployment. We describe the issues we encountered in the implementation. We present times, RAM and ROM needed to run the recovery protocols and compare these with the stationary case, demonstrating that the additional cost of reprogramming in a mobile WSN is less than 25% of that in a stationary WSN and the additional cost of re-clustering in a mobile WSN is less than 9% of that in a stationary WSN. Authentication has an insignificant cost increase.

Recovery mechanism on sensor networks

On the completion of project, we propose novel recovery mechanisms which recovers limited-resource wireless sensor networks quickly from an malicious attack. The research outcomes include re-clustering algorithms, reprogramming techniques and authentications protocols developed and tested on both hardware and simulation platforms. The work is also well compared with other researchers.

His-Purkinje system re-entry in patients with clustering ventricular tachycardia episodes

AIMS: Multiple arrhythmia re-inductions were recently shown in His-Purkinje system (HPS) ventricular tachycardia (VT). We hypothesized that HPS VT was a frequent mechanism of repetitive or incessant VT and assessed diagnostic criteria to select patients likely to have HPS VT. METHODS AND RESULTS: Consecutive patients with clustering VT episodes (>3 sustained monomorphic VT within 2 weeks) were included in the analysis. HPS VT was considered plausible in patients with (i) impaired left ventricular function associated with dilated cardiomyopathy or valvular heart disease; or (ii) ECG during VT similar to sinus rhythm QRS or to bundle-branch block QRS. HPS VT was plausible in 12 of 48 patients and HPS VT was demonstrated in 6 of 12 patients (50%, or 13% of the whole study group). Median VT cycle length was 318 ms (250-550). Catheter ablation was successful in all six patients. CONCLUSION: His-Purkinje system VT is found in a significant number of patients with repetitive or incessant VT episodes, and in a large proportion of patients with predefined clinical or electrocardiographic characteristics. Since it is easily amenable to catheter ablation, our data support the screening of all patients with repetitive VT in this regard and an invasive approach in a selected group of patients.

Quantum clustering and network analysis of MD simulation trajectories to probe the conformational ensembles of protein-ligand interactions

In this article, we present a novel application of a quantum clustering (QC) technique to objectively cluster the conformations, sampled by molecular dynamics simulations performed on different ligand bound structures of the protein. We further portray each conformational population in terms of dynamically stable network parameters which beautifully capture the ligand induced variations in the ensemble in atomistic detail. The conformational populations thus identified by the QC method and verified by network parameters are evaluated for different ligand bound states of the protein pyrrolysyl-tRNA synthetase (DhPylRS) from D. hafniense. The ligand/environment induced re-distribution of protein conformational ensembles forms the basis for understanding several important biological phenomena such as allostery and enzyme catalysis. The atomistic level characterization of each population in the conformational ensemble in terms of the re-orchestrated networks of amino acids is a challenging problem, especially when the changes are minimal at the backbone level. Here we demonstrate that the QC method is sensitive to such subtle changes and is able to cluster MD snapshots which are similar at the side-chain interaction level. Although we have applied these methods on simulation trajectories of a modest time scale (20 ns each), we emphasize that our methodology provides a general approach towards an objective clustering of large-scale MD simulation data and may be applied to probe multistate equilibria at higher time scales, and to problems related to protein folding for any protein or protein-protein/RNA/DNA complex of interest with a known structure.

Efficient spatial clustering algorithm using binary tree

In this paper we present an efficient k-Means clustering algorithm for two dimensional data. The proposed algorithm re-organizes dataset into a form of nested binary tree*. Data items are compared at each node with only two nearest means with respect to each dimension and assigned to the one that has the closer mean. The main intuition of our research is as follows: We build the nested binary tree. Then we scan the data in raster order by in-order traversal of the tree. Lastly we compare data item at each node to the only two nearest means to assign the value to the intendant cluster. In this way we are able to save the computational cost significantly by reducing the number of comparisons with means and also by the least use to Euclidian distance formula. Our results showed that our method can perform clustering operation much faster than the classical ones. © Springer-Verlag Berlin Heidelberg 2005

Detection and classification of anomaly intrusion using hierarchy clustering and SVM

Anomaly detection as a kind of intrusion detection is good at detecting the unknown attacks or new attacks, and it has attracted much attention during recent years. In this paper, a new hierarchy anomaly intrusion detection model that combines the fuzzy c-means (FCM) based on genetic algorithm and SVM is proposed. During the process of detecting intrusion, the membership function and the fuzzy interval are applied to it, and the process is extended to soft classification from the previous hard classification. Then a fuzzy error correction sub interval is introduced, so when the detection result of a data instance belongs to this range, the data will be re-detected in order to improve the effectiveness of intrusion detection. Experimental results show that the proposed model can effectively detect the vast majority of network attack types, which provides a feasible solution for solving the problems of false alarm rate and detection rate in anomaly intrusion detection model.

Wavelet analysis and hierarchical clustering of ofm phenotypic population's variations originated from southern america and europe and attempts of a first relation to molecular divergence.

In this work we compare Grapholita molesta Busck (Lepidoptera: Tortricidae) populations originated from Brazil, Chile, Spain, Italy and Greece using power spectral density and phylogenetic analysis to detect any similarities between the population macro- and the molecular micro-level. Log-transformed population data were normalized and AR(p) models were developed to generate for each case population time series of equal lengths. The time-frequency/scale properties of the population data were further analyzed using wavelet analysis to detect any population dynamics frequency changes and cluster the populations. Based on the power spectral of each population time series and the hierarchical clustering schemes, populations originated from Southern America (Brazil and Chile) exhibit similar rhythmic properties and are both closer related with populations originated from Greece. Populations from Spain and especially Italy, have higher distance by terms of periodic changes on their population dynamics. Moreover, the members within the same cluster share similar spectral information, therefore they are supposed to participate in the same temporally regulated population process. On the contrary, the phylogenetic approach revealed a less structured pattern that bears indications of panmixia, as the two clusters contain individuals from both Europe and South America. This preliminary outcome will be further assessed by incorporating more individuals and likely employed a second molecular marker.