CollECT: Collaborative Event deteCtion and Tracking in Wireless Heterogeneous Sensor Networks

[[abstract]]Event detection and tracking are attractive research issues in the wireless sensor network (WSN). The paper proposes a fully distributed protocol, CollECT, to event detection and tracking in a Wireless Heterogeneous Sensor Network (WHSN), composed of many kinds of sensors. In CollECT, three major procedures, vicinity triangulation, event determination, and border sensor selection are used to construct the logical triangle in the vicinity of a sensor, to determine the event, and to select the border sensor to identify the event boundary, respectively. The procedures perform repeatedly to both detect and track events. Simulation results demonstrate that CollECT is promising for event detection and tracking due to satisfactory event accuracy and reasonable fitness of border sensors.[[conferencetype]]國際[[conferencedate]]20060626~20060629[[iscallforpapers]]Y[[conferencelocation]]Sardinia, Ital

A new sinkhole attack detection algorithm for RPL in wireless sensor networks (WSN)

With the continuous improvement of science and technology, wireless sensor network technology has gradually been widely used, and provides great convenience for people's living, but with the continuous improvement of the degree of application, wireless sensor network security issues also enter people's field of vision. Sensor nodes can be used for continuous sensing, event recognition and event identification. 6LoWPAN plays an important role in this convergence of heterogeneous technologies, which allows sensors to transmit information using IPv6 stack. Sensors perform critical tasks and become targets of attacks. Sinkhole attack is one of the most common attacks to sensor networks, threatening the network availability by dropping data or disturbing routing paths. RPL is a standard routing protocol commonly used in sensor networks. Therefore, this research presents the works in designing and developing Secured-RPL using the eave-listening concept (overhearing) to treating sinkhole attack. The suggested mechanism method could determine transmitted packages then overhear to the received packet, meaning that the node can overhearing to the neighbor node. Furthermore, three different simulation scenarios were applied, which are the scenario without attacker nodes, scenario with attacker nodes and the scenario with attacker and security by using Cooja simulator to Measurement and analysis performance of RPL in terms of packet delivery ratio (PDR) and power consumption over different packet transmission rate. The experimental results show that the proposed recognition method can identify sinkholes attack effectively and with less storage cost under various wireless sensor networks. Where the optimization ratio of the PDR in scenario with attacker node with the security was close to the scenario with a normal node

Un nuevo esquema de agrupación para redes sensoras inalámbricas de radio cognitivas heterogéneas

Introduction: This article is the product of the research “Learning-based Spectrum Analysis and Prediction in Cognitive Radio Sensor Networks”, developed at Sejong University in the year 2019. Problem: Most of the clustering schemes for distributed cognitive radio-enabled wireless sensor networks consider homogeneous cognitive radio-enabled wireless sensors. Many clustering schemes for such homogeneouscognitive radio-enabled wireless sensor networks waste resources and suffer from energy inefficiency because of the unnecessary overheads. Objective: The objective of the research is to propose a node clustering scheme that conserves energy and prolongs network lifetime. Methodology: A heterogeneous cognitive radio-enabled wireless sensor network in which only a few nodes have a cognitive radio module and the other nodes are normal sensor nodes. Along with the hardware cost, theproposed scheme is efficient in energy consumption. Results: We simulated the proposed scheme and compared it with the homogeneous cognitive radio-enabled wireless sensor networks. The results show that the proposed scheme is efficient in terms of energyconsumption. Conclusion: The proposed node clustering scheme performs better in terms of network energy conservation and network partition. Originality: There are heterogeneous node clustering schemes in the literature for cooperative spectrum sensing and energy efficiency, but to the best of our knowledge, there is no study that proposes a non-cognitiveradio-enabled sensor clustering for energy conservation along with cognitive radio-enabled wireless sensors. Limitations: The deployment of the proposed special device for cognitive radio-enabled wireless sensors is complicated and requires special hardware with better battery powered cognitive sensor nodes

Predictive Duty Cycle Adaptation for Wireless Camera Networks

Wireless sensor networks (WSN) typically employ dynamic duty cycle schemes to efficiently handle different patterns of communication traffic in the network. However, existing duty cycling approaches are not suitable for event-driven WSN, in particular, camera-based networks designed to track humans and objects. A characteristic feature of such networks is the spatially-correlated bursty traffic that occurs in the vicinity of potentially highly mobile objects. In this paper, we propose a concept of indirect sensing in the MAC layer of a wireless camera network and an active duty cycle adaptation scheme based on Kalman filter that continuously predicts and updates the location of the object that triggers bursty communication traffic in the network. This prediction allows the camera nodes to alter their communication protocol parameters prior to the actual increase in the communication traffic. Our simulations demonstrate that our active adaptation strategy outperforms TMAC not only in terms of energy efficiency and communication latency, but also in terms of TIBPEA, a QoS metric for event-driven WSN