Current Issue : January - March Volume : 2017 Issue Number : 1 Articles : 5 Articles
Wireless sensor networks (WSNs) have recently gained popularity for a wide spectrum of\napplications. Monitoring tasks can be performed in various environments. This may be beneficial\nin many scenarios, but it certainly exhibits new challenges in terms of security due to increased\ndata transmission over the wireless channel with potentially unknown threats. Among possible\nsecurity issues are timing attacks, which are not prevented by traditional cryptographic security.\nMoreover, the limited energy and memory resources prohibit the use of complex security mechanisms\nin such systems. Therefore, balancing between security and the associated energy consumption\nbecomes a crucial challenge. This paper proposes a secure scheme for WSNs while maintaining the\nrequirement of the security-performance tradeoff. In order to proceed to a quantitative treatment of\nthis problem, a hybrid continuous-time Markov chain (CTMC) and queueing model are put forward,\nand the tradeoff analysis of the security and performance attributes is carried out. By extending and\ntransforming this model, the mean time to security attributes failure is evaluated. Through tradeoff\nanalysis, we show that our scheme can enhance the security of WSNs, and the optimal rekeying rate\nof the performance and security tradeoff can be obtained....
This paper presents an experimental performance assessment for localization systems using\nreceived signal strength (RSS) measurements from a wireless sensor network. In this experimental\nstudy, we compare two types of model-based localization methods: transceiver-based localization,\nwhich locates objects using RSS from transmitters to receivers at known locations; and transceiver-free\nlocalization, which estimates location by using RSS changes on known-location nodes caused by\nobjects. We evaluate their performance using three sets of experiments with different environmental\nconditions. Our performance analysis shows that transceiver-free localization methods are generally\nmore accurate than transceiver-based localization methods for a wireless sensor network with high\nnode density....
This paper is concerned with distributed estimation of a scalar parameter using a wireless sensor network (WSN) that\nemploys a large number of sensors operating under limited bandwidth resource. A semi-orthogonal multiple-access\n(MA) scheme is proposed to transmit observations from K sensors to a fusion center (FC) via N orthogonal channels,\nwhere K � N. The K sensors are divided into N groups, where the sensors in each group simultaneously transmit on\none orthogonal channel (and hence the transmitted signals are directly superimposed at the FC as opposed to be\ncoherently combined). Under such a semi-orthogonal multiple access channel (MAC), performance of the linear\nminimum mean squared error (LMMSE) estimation is analyzed in terms of two indicators: the channel noise\nsuppression capability and the observation noise suppression capability. The analysis is performed for two versions of\nthe proposed semi-orthogonal MA scheme: fixed sensor grouping and adaptive sensor grouping. In particular, the\nsemi-orthogonal MAC with fixed sensor grouping is shown to have the same channel noise suppression capability\nand two times the observation noise suppression capability when compared to the orthogonal MAC under the same\nbandwidth resource. For the semi-orthogonal MAC with adaptive sensor grouping, it is determined that N = 4 is the\nmost favorable number of orthogonal channels when taking into account both performance and feedback\nrequirement. In particular, the semi-orthogonal MAC with adaptive sensor grouping is shown to perform very close to\nthat of the hybrid MAC, while requiring only log2 N = 2 bits of information feedback instead of the exact channel\nphase for each sensor....
Aiming at the ââ?¬Å?hotspotsââ?¬Â problem in energy heterogeneous wireless sensor networks, a routing algorithm of heterogeneous sensor\nnetwork withmultilevel energies based on uneven clustering is proposed. In this algorithm, the energy heterogeneity of the nodes is\nfully reflected in the mechanism of cluster-headsââ?¬â?¢ election. It optimizes the competition radius of the cluster-heads according to the\nresidual energy of the nodes. This kind of uneven clustering prolongs the lifetime of the cluster-heads with lower residual energies\nor near the sink nodes. In data transmission stage, the hybrid multihop transmission mode is adopted, and the next-hop routing\nelection fully takes account of the factors of residual energies and the distances among the nodes. The simulation results show that\nthe introduction of an uneven clustering mechanism and the optimization of competition radius of the cluster-heads significantly\nprolonged the lifetime of the network and improved the efficiency of data transmission....
Wireless Sensor Networks (WSN) have become increasingly one of the hottest research areas in computer science due to their wide\nrange of applications including critical military and civilian applications. Such applications have created various security threats,\nespecially in unattended environments. To ensure the security and dependability ofWSN services, an Intrusion Detection System\n(IDS) should be in place.This IDS has to be compatible with the characteristics ofWSNs and capable of detecting the largest possible\nnumber of security threats. In this paper a specialized dataset forWSN is developed to help better detect and classify four types of\nDenial of Service (DoS) attacks: Blackhole, Grayhole, Flooding, and Scheduling attacks. This paper considers the use of LEACH\nprotocol which is one of the most popular hierarchical routing protocols in WSNs. A scheme has been defined to collect data\nfrom Network Simulator 2 (NS-2) and then processed to produce 23 features. The collected dataset is called WSN-DS. Artificial\nNeural Network (ANN) has been trained on the dataset to detect and classify different DoS attacks. The results show that WSNDS\nimproved the ability of IDS to achieve higher classification accuracy rate. WEKA toolbox was used with holdout and 10-Fold\nCross Validation methods. The best results were achieved with 10-Fold Cross Validation with one hidden layer. The classification\naccuracies of attacks were 92.8%, 99.4%, 92.2%, 75.6%, and 99.8% for Blackhole, Flooding, Scheduling, and Grayhole attacks, in\naddition to the normal case (without attacks), respectively....
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