A sinkhole resilient protocol for wireless sensor networks: Performance and security analysis

International audienceThis work focuses on: (1) understanding the impact of selective forwarding attacks on tree-based routing topologies in wireless sensor networks (WSNs), and (2) investigating cryptography-based strategies to limit network degradation caused by sinkhole attacks. The main motivation of our research stems from the following observations. First, WSN protocols that construct a fixed routing topology may be significantly affected by malicious attacks. Second, considering networks deployed in a difficult to access geographical region, building up resilience against such attacks rather than detection is expected to be more beneficial. We thus first provide a simulation study on the impact of malicious attacks based on a diverse set of parameters, such as the network scale and the position and number of malicious nodes. Based on this study, we propose a single but very representative metric for describing this impact. Second, we present the novel design and evaluation of two simple and resilient topology-based reconfiguration protocols that broadcast cryptographic values. The results of our simulation study together with a detailed analysis of the cryptographic overhead (communication, memory, and computational costs) show that our reconfiguration protocols are practical and effective in improving resilience against sinkhole attacks, even in the presence of collusion

Basic Pattern Matching Calculi: a Fresh View on Matching Failure

Abstract. We propose pattern matching calculi as a refinement of λ-calculus that integrates mechanisms appropriate for fine-grained mod-elling of non-strict pattern matching. Compared with the functional rewriting strategy usually employed to define the operational semantics of pattern matching in non-strict functional programming languages like Haskell or Clean, our pattern matching calculi achieve the same effects using simpler and more local rules. The main device is to embed into expressions the separate syntactic cate-gory of matchings; the resulting language naturally encompasses pattern guards and Boolean guards as special cases. By allowing a confluent reduction system and a normalising strategy, these pattern matching calculi provide a new basis for operational semantics of non-strict programming languages and also for implemen-tations.

On the Prediction of Solar Activity Using Different Neural Network Models

Accurate prediction of ionospheric parameters is crucial for telecommunication companies. These parameters strongly rely on solar activity. In this paper, we analyze the use of neural networks for sunspots time series prediction. Three types of models are tested and experimental results are reported for a particular sunspots time series: the IR5 index

Exploiting semantic clustering in the edonkey p2p network

Peer-to-peer file sharing now represents a significant portion of the Internet traffic and has generated a lot of interest from the research community. Some recent measurements studies of peer-to-peer workloads have demonstrated the presence of semantic proximity between peers. One way to improve performance of peer-to-peer file sharing systems is to exploit this locality of interest in order to connect semantically related peers so as to improve the search both in flooding- and server-based systems. Creating these additional connections raises interesting challenges and in particular (i) how to capture the semantic relationship between peers (ii) how to exploit these relationships and (iii) how to evaluate these improvements. In this paper, we evaluate several strategies to exploit the semantic proximity between peers against a real trace collected in November 2003 in the eDonkey 2000 peer-to-peer network. We present the results of this evaluation which confirm the presence of clustering in such networks and the interest to exploit it. 1 Introduction an

Optimisation of an asymmetric three phase-shift distributed feedback semiconductor laser

This paper shows that accurately optimised asymmetric three phase-shift (3PS)-distributed feedback (DFB) laser structures can strongly improve the stability of the single-longitudinal mode (SLM) operation, described by the mode selectivity and the flatness of the photon density profile, through an extended range of current injection, when compared to optimised symmetric 3PS-DFB structures reported elsewhere. This study reveals its importance in modern high bit-rate optical communication systems, by enhancing the possibility of attaining high performance DFB lasers, in easily fabricated structures. The procedure, based on matrix techniques, aims at the description of the optimal design of the laser structure and it is described step-by-step. Above-threshold calculations have been accomplished to evaluate the performance of the optimised asymmetric 3PS-DFB structure, namely: the mode selectivity ($G$), the flatness, the lasing wavelength, the optical power, and the side-mode suppression ratio (SMSR) evolutions with the current injection. For a current injection five times bigger than the threshold current, substantially improvements in $G$ (five times bigger) and in the SMSR (about 9 dB higher) are achieved when compared to similar, but symmetric, DFB structures

On the prediction of solar activity using different neural network models

Accurate prediction of ionospheric parameters is crucial for telecommunication companies. These parameters rely strongly on solar activity. In this paper, we analyze the use of neural networks for sunspot time series prediction. Three types of models are tested and experimental results are reported for a particular sunspot time series: the <i>IR</i>5 index