In cluster-based wireless sensor networks, cluster heads (CHs) gather and fuse data packets from sensor nodes;then, they forward fused packets to the sink node (SN). This helps wireless sensor networks balance energy ...
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In cluster-based wireless sensor networks, cluster heads (CHs) gather and fuse data packets from sensor nodes;then, they forward fused packets to the sink node (SN). This helps wireless sensor networks balance energy effectively and efficiently to prolong their lifetime. However, cluster-based WSNs are vulnerable to selective forwarding attacks. Compromised CHs would become malicious and launch selective forwarding attacks in which they drop part of or all the packets from other nodes. In this paper, a data clustering algorithm (DCA) for detecting a selective forwarding attack (DCA-SF) is proposed. It can capture and isolate malicious CHs that have launched selective forwarding attacks by clustering their cumulative forwarding rates (CFRs). The DCA-SF algorithm has been strengthened by changing the DCA parameters (Eps, Minpts) adaptively. The simulation results show that the DCA-SF has a low missed detection rate of 1.04% and a false detection rate of 0.42% respectively with low energy consumption.
The detection of signals and the estimation of signal bandwidth is a perpetual topic in radio communication systems. Both issues are extremely challenging, since the wireless channel is unreliable in nature. A radio m...
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The detection of signals and the estimation of signal bandwidth is a perpetual topic in radio communication systems. Both issues are extremely challenging, since the wireless channel is unreliable in nature. A radio monitoring system faces the most difficult conditions in this task;it normally scans a wide frequency range of several hundred MHz and has to detect a multitude of different signals. Owing to the computational costs, the radio monitoring systems use nowadays mainly energy detectors based on fast Fourier transform spectrum analysers and a static threshold, defined by a previous noise estimation. A refined algorithm based on the self-splitting competitive learning (SSCL) clustering is presented that quantises the power spectral density (PSD) according to the present signal power levels. The quantisation of the PSD results in a promising channel segmentation. In contrast to the traditional threshold evaluation, this approach is independent of a previously assumed noise estimation and therefore more robust against noise level and noise distribution changes. The presented definition of the essential cluster validity criterion is key for a successful channel segmentation. Furthermore, the novel postprocessing of the clustering result introduced in this study evaluates the progression of the PSD data and significantly improves the channel segmentation.
Wireless sensor networks (WSNs) are extremely vulnerable to different attacks because of open communication, and distribution in unattended areas. The selective forwarding attack is one of the most difficult inside at...
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Wireless sensor networks (WSNs) are extremely vulnerable to different attacks because of open communication, and distribution in unattended areas. The selective forwarding attack is one of the most difficult inside attacks to be detected for two reasons. The node in a harsh environment has to drop some data packets, and the smart malicious node frequently eludes detection. In this paper, we model a selective forwarding attack of smart malicious nodes with a reinforcement learning (RL) algorithm. To effectively detect the selective forwarding attack under a harsh environment, we design the double-threshold density peaks clustering (DT-DPC) algorithm. Abnormal nodes are identified as malicious and isolated owing to continuous abnormalities. Suspicious nodes are determined by the neighbor voting method because malicious behaviors show up separately and a harsh environment universally disturbs agglomerate nodes. Even if smart malicious nodes elude the detection by an RL algorithm, DT-DPC improves the network throughput. The simulation results show that DT-DPC has a low false detection rate (FDR) of around 1% and a missed detection rate (MDR) of around 10%. The network throughput increases by about 4% under a harsh environment.
Wireless sensor networks (WSNs) are susceptible to numerous security threats due to their reliance on open environments and broadcast communication methods. Among these, the selective forwarding attack is notably chal...
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Wireless sensor networks (WSNs) are susceptible to numerous security threats due to their reliance on open environments and broadcast communication methods. Among these, the selective forwarding attack is notably challenging to detect. This difficulty arises from the ability of malicious nodes to imitate the behavior of normal nodes, and selectively drop data packets, which makes them virtually indistinguishable from normal ones, particularly under conditions of poor channel quality. To address this challenge with harsh environments, we introduce a novel methodology termed GD3N. This approach is underpinned by the design of a unique type of data point that encapsulates both short-term and long-term forwarding behaviors of nodes. It combines a refined version of the Gradient Diffusion Density -based Spatial clustering of Applications with Noise (GD-DBSCAN) algorithm, with a novel Double -Parameter Neighbor Voting (DP -NV) method based on the data set. These innovations contribute to a significant enhancement in detection accuracy and a reduction in computational complexity when compared to traditional DBSCAN and NV methods. Simulation results show that our GD3N achieves a false detection rate (FDR) of less than 2%, a missed detection rate (MDR) of below 10%, and an overall detection accuracy rate (DAR) of over 95% across various testing scenarios.
In the event-driven wireless sensor networks (EWSNs), the event of interests occurs irregularly and at random in the network. Then, sensor nodes near the event sense the event and send out data packets of the event. N...
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In the event-driven wireless sensor networks (EWSNs), the event of interests occurs irregularly and at random in the network. Then, sensor nodes near the event sense the event and send out data packets of the event. Next, router nodes (RNs) forward those packets to the sink node (SN) by multi-hop communications. Compromised RNs would become malicious and launch selective forwarding attacks by dropping part of or all the packets from other nodes. On the other hand, a harsh environment makes the channel poor, so the routing nodes under a harsh environment have low packet forwarding rates because they sometimes have to give up forwarding the current packets after many tries to forward them due to poor channel. If the malicious nodes' forwarding rates become close to those of nodes under a harsh environment, the schemes based on packet forwarding rates for detecting selective forwarding attack may fail because they cannot differentiate the low data packet forwarding rates resulting from the malicious behaviors or harsh environment. To solve this problem, we provide a combined scheme for detecting selective forwarding attack in wireless sensor networks (WSNs) under harsh environments. This scheme employs a data clustering algorithm (DCA) to screen the malicious nodes out by clustering their cumulative forwarding rates (CFRs) and designs a voting decision method to protect the nodes under a harsh environment from being judged as malicious nodes. The simulation results show that our scheme has a low false detection rate (FDR) of 1% and a low missed detection rate (MDR) of 5% respectively with negligible energy consumption in WSNs under a local variable harsh environment.
This paper presents an application with record performance for electromagnetic spectrum analysis of multispectral satellite image. The analysis method is an application-specific pixels oriented to images segmentation....
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ISBN:
(纸本)9783319951621;9783319951614
This paper presents an application with record performance for electromagnetic spectrum analysis of multispectral satellite image. The analysis method is an application-specific pixels oriented to images segmentation. This kind of segmentation is used in remote sensing for land cover and land use classification and change detection. Regions of the image are clustered separately and then the results are combined, for this the processing method employs two types of clusteringalgorithms, each specialized to its task and steered towards obtaining a final meaningful segmentation. The results show good spatial coherency in segments and coherent borders between regions that were segmented separately.
For communication distance estimations in Wireless Sensor Networks (WSNs), the RSSI (Received Signal Strength Indicator) value is usually assumed to have a linear relationship with the logarithm of the communication d...
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For communication distance estimations in Wireless Sensor Networks (WSNs), the RSSI (Received Signal Strength Indicator) value is usually assumed to have a linear relationship with the logarithm of the communication distance. However, this is not always true in reality because there are always uncertainties in RSSI readings due to obstacles, wireless interferences, etc. In this paper, we specifically propose a novel RSSI-based communication distance estimation method based on the idea of interval dataclustering. We first use interval data, combined with statistical information of RSSI values, to interpret the distribution characteristics of RSSI. We then use interval data hard clustering and soft clustering to overcome different levels of RSSI uncertainties, respectively. We have used real RSSI measurements to evaluate our communication distance estimation method in three representative wireless environments. Extensive experimental results show that our communication distance estimation method can effectively achieve promising estimation accuracy with high efficiency when compared to other state-of-art approaches.
Over the past decade, Big data has been becoming a great research hotspot because of continuous implementation of advanced techniques, burgeoning interdisciplinary cooperation and varying user requirements. Because of...
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ISBN:
(纸本)9783037858417
Over the past decade, Big data has been becoming a great research hotspot because of continuous implementation of advanced techniques, burgeoning interdisciplinary cooperation and varying user requirements. Because of its well-known four V-characters, the associated applications always suffer from low efficiency and hard to manage. Our research summarized the common issues of Big data-based applications, and set improving data formatting and representation performances as the research objectives. In this paper, a novel data presentation strategy was built via devising volume-based representation to facilitate complicated processing work and overcome limitations of data manipulation tasks. For improving information processing efficiency, this design served as a data carrier which enables flexible implementations of data processing algorithms. Besides, its inherent spatial information not only supports direct operations, but shows the feasibility of information integration in the future work.
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