Indoor Localization by using Particle Filtering Approach with Wireless Sensor Nodes

Jennic type wireless sensor nodes are utilized together with a novel particle filtering technique for indoor localization. Target objects are localized with an accuracy of around 0.25 meters. The proposed technique introduces a new particle generation and distribution technique to improve current estimation of object positions. Particles are randomly distributed around the object in the sensing area within a circular strip of 2 STD of object distance measurements. Particle locations are related to object locations by using Gaussian weight distribution methods. Object distances from the transmitters are determined by using received RSSI values and ITU-R indoor propagation model. Measured object distances are used together with the particle distances from the transmitters to predict the object locations.

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Modelling the Spread of Botnet Malware in IoT-Based Wireless Sensor Networks

Security and Communication Networks ◽

10.1155/2019/3745619 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 13

Author(s):

Dilara Acarali ◽

Muttukrishnan Rajarajan ◽

Nikos Komninos ◽

B. B. Zarpelão

Keyword(s):

Real Life ◽

Influential Factors ◽

Propagation Model ◽

Sensor Nodes ◽

Wireless Sensor ◽

Wireless Sensor Nodes ◽

Processing Power ◽

The Monte Carlo Method ◽

Attack Surface ◽

The Impact

The propagation approach of a botnet largely dictates its formation, establishing a foundation of bots for future exploitation. The chosen propagation method determines the attack surface and, consequently, the degree of network penetration, as well as the overall size and the eventual attack potency. It is therefore essential to understand propagation behaviours and influential factors in order to better secure vulnerable systems. Whilst botnet propagation is generally well studied, newer technologies like IoT have unique characteristics which are yet to be thoroughly explored. In this paper, we apply the principles of epidemic modelling to IoT networks consisting of wireless sensor nodes. We build IoT-SIS, a novel propagation model which considers the impact of IoT-specific characteristics like limited processing power, energy restrictions, and node density on the formation of a botnet. Focusing on worm-based propagation, this model is used to explore the dynamics of spread using numerical simulations and the Monte Carlo method to discuss the real-life implications of our findings.

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A Sub-A Ultrasonic Wake-Up Trigger with Addressing Capability for Wireless Sensor Nodes

ISRN Sensor Networks ◽

10.1155/2013/720817 ◽

2013 ◽

Vol 2013 ◽

pp. 1-10 ◽

Cited By ~ 11

Author(s):

Emanuele Lattanzi ◽

Matteo Dromedari ◽

Valerio Freschi ◽

Alessandro Bogliolo

Keyword(s):

Degrees Of Freedom ◽

State Diagram ◽

Propagation Speed ◽

Sensor Nodes ◽

Wireless Sensor ◽

Dynamic Power Management ◽

Distance Measurements ◽

Wireless Sensor Nodes ◽

Ultra Low Power ◽

Ultrasonic Signals

Wireless sensor nodes spend most of the time waiting either for sensed data or for packets to be routed to the sink. While on board, sensors can raise hardware interrupts to trigger the wake-up of the processor, incoming packets require the radio module to be turned on in order to be properly received and processed; thus, reducing the effectiveness of dynamic power management and exposing the node to unintended packets cause energy waste. The capability of triggering the wake-up of a node over the air would makes it possible to keep the entire network asleep and to wake up the nodes along a path to the sink whenever there is a packet to transmit. This paper presents an ultrasonic wake-up trigger for ultra-low-power wireless sensor nodes developed as a plug-in module for VirtualSense motes. The module supports a simple out-of-band addressing scheme to enable the selective wake-up of a target node. In addition, it makes it possible to exploit the propagation speed of ultrasonic signals to perform distance measurements. The paper outlines the design choices, reports the results of extensive measurements, and discusses the additional degrees of freedom introduced by ultrasonic triggering in the power-state diagram of VirtualSense.

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Number of sensors versus time to detection in wildfires

International Journal of Wildland Fire ◽

10.1071/wf07137 ◽

2009 ◽

Vol 18 (7) ◽

pp. 825 ◽

Cited By ~ 5

Author(s):

Pablo I. Fierens

Keyword(s):

Probability Distribution ◽

Propagation Model ◽

Sensor Nodes ◽

Wireless Sensor ◽

Independent Random Variables ◽

Burned Area ◽

Wireless Sensor Nodes ◽

Time To Detection ◽

The Moment ◽

The Cost

The lack of extensive research in the application of inexpensive wireless sensor nodes for the early detection of wildfires motivated us to investigate the cost of such a network. As a first step, in this paper we present several results that relate the time to detection and the burned area to the number of sensor nodes in the region that is protected. We prove that the probability distribution of the size of the burned area at the moment of detection is approximately exponential, given that some hypotheses hold: the positions of the sensor nodes are independent random variables uniformly distributed and the number of sensor nodes is large. This conclusion depends neither on the number of ignition points nor on the propagation model of the fire.

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