Resilient Sensor Networks Research Articles

Design of Resilient Sensor Networks Balancing Resilience and Efficiency

In recent years, the notion of resilience has been developed and applied in many technical areas, becoming exceptionally pertinent to disaster risk science. During a disaster situation, accurate sensing information is the key to efficient recovery efforts. In general, resilience aims to minimize the impact of disruptions to systems through the fast recovery of critical functionality, but resilient design may require redundancy and could increase costs. In this article, we describe a method based on binary linear programming for sensor network design balancing efficiency with resilience. The application of the developed framework is demonstrated for the case of interior building surveillance utilizing infrared sensors in both two- and three-dimensional spaces. The method provides optimal sensor placement, taking into account critical functionality and a desired level of resilience and considering sensor type and availability. The problem formulation, resilience requirements, and application of the optimization algorithm are described in detail. Analysis of sensor locations with and without resilience requirements shows that resilient configuration requires redundancy in number of sensors and their intelligent placement. Both tasks are successfully solved by the described method, which can be applied to strengthen the resilience of sensor networks by design. The proposed methodology is suitable for large-scale optimization problems with many sensors and extensive coverage areas.

Multisource wireless energy harvesting-based medium access control for rechargeable sensors

By collecting data from sensor devices, wireless sensor networks enable consumer product management in indoor environments. However, most off-the-shelf sensor devices are battery-powered and hence hampered by the limitation of battery life. In this context, wireless rechargeable sensor networks (WRSNs) which adopt wireless power transfer (WPT) technique - leveraging electromagnetic waves for sensor energy replenishment - have emerged as a promising scenario for the architecture of self-sustainable and resilient sensor networks. Nevertheless, while the rapid proliferation of studies on discussion WPT and data collection in WRSNs has been witnessed in recent years, their unilateral investigation makes them insufficient to construct high-performance WRSNs. Therefore, this paper firstly explores joint WPT and data collection in a WRSN and accordingly presents a medium access control protocol called FarMac. The WRSN employs multiple sink nodes which can either conduct WPT to or data collection from numerous deployed sensor devices. The sensor devices, on the other hand, fall into two categories: lethargic sensor device which needs energy replenishment from the sink nodes before conveying its data and energetic sensor device which can disseminate its data directly. In this context, FarMac leverages a centralized algorithm to achieve multisource WPT for maximizing the transferred power to a lethargic sensor device. In addition, each lethargic sensor device executes a distributed algorithm to compute its necessary energy harvesting time. Furthermore, FarMac achieves concurrent WPT and data collection through interference cancellation technique. Simulation results demonstrate that FarMac improves network throughput by up to 41% compared with a benchmark approach and guarantees network resilience <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .

Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites.

This paper attempts to construct a resilient sensor network model with an example of space weather forecasting. The proposed model is based on a dynamic relational network. Space weather forecasting is vital for a satellite operation because an operational team needs to make a decision for providing its satellite service. The proposed model is resilient to failures of sensors or missing data due to the satellite operation. In the proposed model, the missing data of a sensor is interpolated by other sensors associated. This paper demonstrates two examples of space weather forecasting that involves the missing observations in some test cases. In these examples, the sensor network for space weather forecasting continues a diagnosis by replacing faulted sensors with virtual ones. The demonstrations showed that the proposed model is resilient against sensor failures due to suspension of hardware failures or technical reasons.

Toward Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Data: An Example of Space Weather Forecasting

Abstract This paper attempts to construct a resilient sensor network model for space weather forecasting. The proposed model is based on a dynamic relational network. A space weather forecasting is vital for a satellite operation because an operational team needs to make a decision for providing its satellite service. The proposed model is resilient for failures of sensors/missing data due to the satellite operation. In the proposed model, the missing data of a sensor is interpolated by other sensors associated. This paper demonstrates an example of the space weather forecasting involving the missing of the observation in a test case. In this example, the sensor network of the space weather forecasting continues a diagnosis by replacing faulted sensors with imaginary ones. The demonstrations showed that the proposed model is resilient against sensor failures due to suspend of hardware failures or technical reasons.

A randomized energy-conservation protocol for resilient sensor networks*

In this paper we present PEAS, a randomized energy-conservation protocol that seeks to build resilient sensor networks in the presence of frequent, unexpected node failures. PEAS extends the networ...