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The adoption of emergency alerting systems can bring countless benefits when managing urban areas, industrial plants, farms, roads and virtually any area that is subject to the occurrence of critical events, supporting in rescue operations and reducing their negative impacts. For such systems, a promising approach is to exploit scalar sensors to detect events of interest, allowing for the distributed monitoring of different variables. However, the use of cameras as visual sensors can enhance the detection of critical events, which can be employed along with scalar sensors for a more comprehensive perception of the environment. Although the particularities of visual sensing may be challenging in some scenarios, the combination of scalar and visual sensors for the early detection of emergency situations can be valuable for many scenarios, such as smart cities and industry 4.0, bringing promising results. Therefore, in this article, we extend a sensors-based emergency detection and alerting system to also exploit visual monitoring when identifying critical events. Implementation and experimental details are provided to reinforce the use of cameras as a relevant sensor unit, bringing promising results for emergencies management.

When camera-enabled sensors are deployed for visual monitoring, a new set of innovative applications is allowed, enriching the use of wireless sensor network technologies. In these networks, energy-efficiency is a highly desired optimization issue, mainly because transmission of images and video streams over resource-constrained sensor networks is more stringent than transmission of conventional scalar data. Due to the nature of visual monitoring, that follows a directional sensing model, camera-enabled sensors may have different relevancies for the application, according to the desired monitoring tasks and the current sensors’ poses and fields of view. Exploiting this concept, each data packet may be associated with a priority level related to the packet’s origins, which may be in turn mapped to an energy threshold level. In such way, we propose an energy-efficient relaying mechanism where data packets are only forwarded to the next hop if the associated energy threshold level is below the current energy level of the relaying node. Thus, packets from low-relevant source nodes will be silently dropped when the current energy level of intermediate nodes run below the pre-defined thresholds. Doing so, energy is saved potentially prolonging the network lifetime. Besides the sensing relevancies of source nodes, the relevance of DWT subbands for reconstruction of original images is also considered. This allows the creation of a second level of packet prioritization, assuring a minimal level of image quality even for the least relevant source nodes. We performed simulations for the proposed relaying mechanism, assessing the expected performance over a traditional relaying paradigm.

The operation of Wireless Sensor Networks (WSNs) is subject to multiple constraints, among which one of the most critical is available energy. Sensor nodes are typically powered by electrochemical batteries. The stored energy in battery devices is easily influenced by the operating temperature and the discharge current values. Therefore, it becomes difficult to estimate their voltage/charge behavior over time, which are relevant variables for the implementation of energy-aware policies. Nowadays, there are hardware and/or software approaches that can provide information about the battery operating conditions. However, this type of hardware-based approach increases the battery production cost, which may impair its use for sensor node implementations. The objective of this work is to propose a software-based approach to estimate both the state of charge and the voltage of batteries inWSN nodes based on the use of a temperature-dependent analytical battery model. The achieved results demonstrate the feasibility of using embedded analytical battery models to estimate the lifetime of batteries, without affecting the tasks performed by the WSN nodes.