João Marco

Abstract
Currently deployed in a wide variety of applicational scenarios, wireless sensor networks (WSNs) are typically a resource-constrained infrastructure. Consequently, characteristics such as WSN adaptability, low-overhead, and low-energy consumption are particularly relevant in dynamic and autonomous sensing environments where the measuring requirements change and human intervention is not viable. To tackle this issue, this article proposes e-LiteSense as an adaptive, energy-aware sensing solution for WSNs, capable of auto-regulate how data are sensed, adjusting it to each applicational scenario. The proposed adaptive scheme is able to maintain the sensing accuracy of the physical phenomena, while reducing the overall process overhead. In this way, the adaptive algorithm relies on low-complexity rules to establish the sensing frequency weighting the recent drifts of the physical parameter and the levels of remaining energy in the sensor. Using datasets from WSN operational scenarios, we prove e-LiteSense effectiveness in self-regulating data sensing accurately through a low-overhead process where the WSN energy levels are preserved. This constitutes a step-forward for implementing self-adaptive energy-aware data sensing in dynamic WSN environments. © 2019 John Wiley & Sons, Ltd.

Abstract
Monitoring current communication networks and services is an increasingly complex task as a result of a growth in the number and variety of components involved. Moreover, different perspectives on network monitoring and optimisation policies must be considered to meet context-dependent monitoring requirements. To face these demanding expectations, this article proposes a semantic-based approach to support the flexible configuration of context-aware network monitoring, where traffic sampling is used to improve efficiency. Thus, a semantic layer is proposed to provide with a standard and interoperable description of the elements, requirements and relevant features in the monitoring domain. On top of this description, semantic rules are applied to make decisions regarding monitoring and auditing policies in a proactive and context-aware manner. Use cases focusing on traffic accounting and traffic classification as monitoring tasks are also provided, demonstrating the expressiveness of the ontology and the contribution of smart SWRL rules for recommending optimised configuration profiles. © 2020, Springer-Verlag GmbH Austria, part of Springer Nature.

Abstract

Abstract

Abstract
In Wireless Sensor Networks (WSN), typically composed of nodes with resource constraints, leveraging efficient processes is crucial to enhance the network lifetime and, consequently, the sustainability in ultra-dense and heterogeneous environments, such as smart cities. Particularly, balancing the energy required to transport data efficiently across such dynamic environments poses significant challenges to routing protocol design and operation, being the trade-off of reducing data redundancy while achieving an acceptable delivery rate a fundamental research topic. In this way, this work proposes a new energy-aware epidemic protocol that uses the current state of the network energy to create a dynamic distribution topology by self-adjusting each node forwarding behavior as eager or lazy according to the local residual battery. Simulated evaluations demonstrate its efficiency in energy consumption, delivery rate, and reduced computational burden when compared with classical gossip protocols as well as with a directional protocol.