In wireless sensor networks (WSNs), congestion occurs, for example, when nodes are densely distributed, and/or the application produces high flow rate near the sink due to the convergent nature of upstream traffic. Congestion may cause packet loss, which in turn lowers throughput and wastes energy. Therefore congestion in WSNs needs to be controlled for high energy-efficiency, to prolong system lifetime, improve fairness, and improve quality of service (QoS) in terms of throughput (or link utilization) and packet loss ratio along with the packet delay. This paper proposes a node priority-based congestion control protocol (PCCP) for wireless sensor networks. In PCCP, node priority index is introduced to reflect the importance of each node. PCCP uses packet interarrival time along with packet service time to measure a parameter defined as congestion degree and furthermore imposes hop-by-hop control based on the measured congestion degree as well as the node priority index. PCCP controls congestion faster and more energy-efficienty than other known techniques.
Abstract-Trustworthy location information is important because it is a critical input to a wide variety of locationbased applications. However, the localization infrastructure is vulnerable to physical attacks and consequently the localization results are affected. In this paper, we focus on achieving robust wireless localization when attacks are present on access points. We first investigate the effects of attacks on localization. We then derive an attack-resistant scheme that can be integrated with existing localization algorithms and are not algorithmspecific. Our attack-resistant scheme are based on K-means clustering analysis. We examined our approach using received signal strength (RSS) in widely used lateration-based algorithms. We validated our method in the ORBIT testbed with an IEEE 802.11 (Wi-Fi) network. Our experimental results demonstrate that our proposed approach can achieve comparable localization performance when under access-point attacks as compared to normal situations without attack.
Electro-optic (EO) image sensors exhibit the properties of high resolution and low noise level at daytime, but they do not work in dark environments. Infrared (IR) image sensors exhibit poor resolution and cannot separate objects with similar temperature. Therefore, we propose a novel framework of IR image enhancement based on the information (e.g., edge) from EO images, which improves the resolution of IR images and helps us distinguish objects at night. Our framework superimposing/blending the edges of the EO image onto the corresponding transformed IR image improves their resolution. In this framework, we adopt the theoretical point spread function (PSF) proposed by Hardie et al. for the IR image, which has the modulation transfer function (MTF) of a uniform detector array and the incoherent optical transfer function (OTF) of diffraction-limited optics. In addition, we design an inverse filter for the proposed PSF and use it for the IR image transformation. The framework requires four main steps: (1) inverse filter-based IR image transformation; (2) EO image edge detection; (3) registration; and (4) blending/superimposing of the obtained image pair. Simulation results show both blended and superimposed IR images, and demonstrate that blended IR images have better quality over the superimposed images. Additionally, based on the same steps, simulation result shows a blended IR image of better quality when only the original IR image is available.
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