Connected coverage with rapid forwarding in energy harvesting wireless sensor networks for critical rare events

<p>To ensure event detection and subsequent rapid forwarding of notification messages, wireless sensor networks deployed to detect critically important rarely occurring events must maintain both sensing coverage and low latency network connectivity at all times. Maintaining coverage for extended periods is relatively straight forward as passive sensing components tend to consume little energy. Maintenance of network connectivity, however, requires sensing devices constantly supply power to their transceivers, significantly reducing the longevity of the sensor network. Energy harvesting can extend the operational life of sensing devices with always on transceivers, potentially to the point where they can operate year round. In addition, over populating the sensing area with more devices than are required to provide complete sensing cover introduces the possibility of self-organisation where sensing devices agree amongst themselves which will remain active and which will be allowed to sleep. Few algorithms have been proposed to address both coverage and forwarding; those that do are either unconcerned with rapid propagation or have not been optimised to handle the constant changes in topology observed in duty cycling networks. This thesis first analyses the energy consumption profiles of commercially available wireless sensing devices then presents mechanisms by which these devices can both maintain sensing coverage and rapidly forward event detection messages delayed only by the inherent latencies found in wireless multi-hop networks. These individual contributions form the basis of a combined algorithm for Coverage Preservation with Rapid Forwarding (CPRF). Through evaluations including live deployment, CPRF is shown to deliver perfect coverage maintenance and low latency message propagation whilst allowing stored-charge conservation via collaborative duty cycling in energy harvesting networks.</p>

Connected coverage with rapid forwarding in energy harvesting wireless sensor networks for critical rare events

<p>To ensure event detection and subsequent rapid forwarding of notification messages, wireless sensor networks deployed to detect critically important rarely occurring events must maintain both sensing coverage and low latency network connectivity at all times. Maintaining coverage for extended periods is relatively straight forward as passive sensing components tend to consume little energy. Maintenance of network connectivity, however, requires sensing devices constantly supply power to their transceivers, significantly reducing the longevity of the sensor network. Energy harvesting can extend the operational life of sensing devices with always on transceivers, potentially to the point where they can operate year round. In addition, over populating the sensing area with more devices than are required to provide complete sensing cover introduces the possibility of self-organisation where sensing devices agree amongst themselves which will remain active and which will be allowed to sleep. Few algorithms have been proposed to address both coverage and forwarding; those that do are either unconcerned with rapid propagation or have not been optimised to handle the constant changes in topology observed in duty cycling networks. This thesis first analyses the energy consumption profiles of commercially available wireless sensing devices then presents mechanisms by which these devices can both maintain sensing coverage and rapidly forward event detection messages delayed only by the inherent latencies found in wireless multi-hop networks. These individual contributions form the basis of a combined algorithm for Coverage Preservation with Rapid Forwarding (CPRF). Through evaluations including live deployment, CPRF is shown to deliver perfect coverage maintenance and low latency message propagation whilst allowing stored-charge conservation via collaborative duty cycling in energy harvesting networks.</p>

A Novel Bi-Tuning SSO Algorithm for Optimizing the Budget-Limited Sensing Coverage Problem in Wireless Sensor Networks

The wireless sensor network (WSN) plays an essential role in various practical smart applications, e.g., smart grids, smart factories, Internet of Things, and smart homes, etc. WSNs are comprised and embedded wireless smart sensors. With advanced developments in wireless sensor networks research, sensors have been rapidly used in various fields. In the meantime, the WSN performance depends on the coverage ratio of the sensors being used. However, the coverage of sensors generally relates to their cost, which usually has a limit. Hence, a new bi-tuning simplified swarm optimization (SSO) is proposed that is based on the SSO to solve such a budget-limited WSN sensing coverage problem to maximize the number of coverage areas to improve the performance of WSNs. The proposed bi-tuning SSO enhances SSO by integrating the novel concept to tune both the SSO parameters and SSO update mechanism simultaneously. The performance and applicability of the proposed bi-tuning SSO using seven different parameter settings are demonstrated through an experiment involving nine WSN tests ranging from 20, 100, to 300 sensors. The proposed bi-tuning SSO outperforms two state-of-the-art algorithms: genetic algorithm (GA) and particle swarm optimization (PSO), and can efficiently accomplish the goals of this work.

Data Reconstruction Coverage Based on Graph Signal Processing for Wireless Sensor Networks

Sensing coverage is a crucial metric for the quality of service of Wireless Sensor Networks (WSNs). Coverage models have a great impact on sensing coverage of WSNs. However, existing coverage models are simple but inefficient, like the most frequently used disk coverage model, in which a covered point is within the fixed sensing radius of at least one sensor node. Thus, how to develop an efficient coverage model is an essential problem. To this end, in this letter, we propose a novel coverage model without the limitation of the sensor’s sensing radius, namely, Data Reconstruction Coverage (DRC). Based on the theory of graph signal processing, the model can jointly reconstruct missing data at unsampled points (which are not covered by any sensors) by using our proposed centralized data reconstruction coverage algorithm which fully exploits the smoothness of temporal difference signals and the graph Laplacian matrix, without increasing the number of sensors. Simulation results based on real-world datasets show that the proposed DRC model has better coverage performance of WSNs compared with the disk coverage model and confident information coverage model typically used in WSNs.

Data Reconstruction Coverage Based on Graph Signal Processing for Wireless Sensor Networks

Sensing coverage is a crucial metric for the quality of service of Wireless Sensor Networks (WSNs). Coverage models have a great impact on sensing coverage of WSNs. However, existing coverage models are simple but inefficient, like the most frequently used disk coverage model, in which a covered point is within the fixed sensing radius of at least one sensor node. Thus, how to develop an efficient coverage model is an essential problem. To this end, in this letter, we propose a novel coverage model without the limitation of the sensor’s sensing radius, namely, Data Reconstruction Coverage (DRC). Based on the theory of graph signal processing, the model can jointly reconstruct missing data at unsampled points (which are not covered by any sensors) by using our proposed centralized data reconstruction coverage algorithm which fully exploits the smoothness of temporal difference signals and the graph Laplacian matrix, without increasing the number of sensors. Simulation results based on real-world datasets show that the proposed DRC model has better coverage performance of WSNs compared with the disk coverage model and confident information coverage model typically used in WSNs.