In this paper, we propose a self-adaptive image transmission scheme driven by energy efficiency considerations in order to be suitable for wireless sensor networks. It is based on wavelet image transform and semi-reliable transmission to achieve energy conservation. Wavelet image transform provides data decomposition in multiple levels of resolution, so the image can be divided into packets with different priorities. Semireliable transmission enables priority-based packet discarding by intermediate nodes according to their battery's state-of-charge. Such an image transmission approach provides a graceful trade-off between the reconstructed images quality and the sensor nodes' lifetime.An analytical study in terms of dissipated energy is performed to compare the selfadaptive image transmission scheme to a fully reliable scheme. Since image processing is computationally intensive and operates on a large data set, the cost of the wavelet image transform is considered in the energy consumption analysis. Results show up to 80% reduction in the energy consumption acheived by our proposal compared to a non energy-aware one, with the guarantee for the image quality to be lower-bounded.

In this paper, we propose two image transmission schemes driven by energy efficiency considerations in order to be suitable for wireless sensor networks. The first one is an open-loop image transmission scheme while the second one is closed-loop. Both schemes are based on wavelet image transform and semi-reliable transmission to achieve energy conservation. Wavelet image transform provides data decomposition in multiple levels of resolution, so the image can be divided into packets with different priorities. Semi-reliable transmission enables priority-based packet discarding by intermediate nodes according to their battery's state-of-charge. Such an image transmission approach provides a graceful trade-off between the image quality played out and the sensor nodes' lifetime.An analytical study in terms of dissipated energy is performed to compare our two schemes to a fully reliable image transmission scheme. Since image processing is computationally intensive and operates on a large data set, the cost of the wavelet image transform is considered in the energy consumption analysis. Results show up to 70% and 90% reductions in energy consumption with the open-loop and closed-loop schemes respectively compared to a non energy-aware one, with a guarantee for the image quality to be lower-bounded.

Modern cities are subject to periodic or unexpected critical events, which may bring economic losses or even put people in danger. When some monitoring systems based on wireless sensor networks are deployed, sensing and transmission configurations of sensor nodes may be adjusted exploiting the relevance of the considered events, but efficient detection and classification of events of interest may be hard to achieve. In Smart City environments, several people spontaneously post information in social media about some event that is being observed and such information may be mined and processed for detection and classification of critical events. This article proposes an integrated approach to detect and classify events of interest posted in social media, notably in Twitter, and the assignment of sensing priorities to source nodes. By doing so, wireless sensor networks deployed in Smart City scenarios can be optimized for higher efficiency when monitoring areas under the influence of the detected events.

With the increasing availability of affordable open-source embedded hardware platforms, the development of low-cost programmable devices for uncountable tasks has accelerated in recent years. In this sense, the large development community that is being created around popular platforms is also contributing to the construction of Internet of Things applications, which can ultimately support the maturation of the smart-cities era. Popular platforms such as Raspberry Pi, BeagleBoard and Arduino come as single-board open-source platforms that have enough computational power for different types of smart-city applications, while keeping affordable prices and encompassing many programming libraries and useful hardware extensions. As a result, smart-city solutions based on such platforms are becoming common and the surveying of recent research in this area can support a better understanding of this scenario, as presented in this article. Moreover, discussions about the continuous developments in these platforms can also indicate promising perspectives when using these boards as key elements to build smart cities.

New applications of wireless sensor networks require vision capabilities. Considering the high loss rates found in sensor networks, and the limited hardware resources of current sensor nodes, low-complexity robust image transmission must be implemented, avoiding as much as possible the need for retransmission or redundancy. In this paper we propose a pixel interleaving scheme based in Torus Automorphisms, thus, neighboring pixels are transmitted in different packets. Hence, if packets are lost, we have a high probability of retrieving enough information to obtain an approximation of the original value. Results show an increase of the image quality in comparison with a sequential raw image transmission approach, while preserving similar energy consumptions, time and low-complexity.