## 1 Introduction  
In recent years, Wireless Sensor Networks (WSNs) have raised a lot of attention of the industry and academic communities [1], addressing applications as surveillance, tracking, disaster monitoring, home automation, industrial control, battlefield surveillance, among others. Typically, WSNs are composed of many self-organizing disposable electronic devices equipped with a short-range wireless transceiver, limited energy supply (usually batteries), a sensing unity and processing and memory resources [2, 3]. When deployed over an area of interest, sensors can collect scalar information as humidity, pressure, temperature, luminosity and seismic variations, according to the application requirements and the nature of the sensing unit endowed in each source node [4, 5]. The use of low-cost, low-power and low-resolution cameras to collect visual information can strongly enhance the monitoring capability of such networks, allowing for the development of Video-based Wireless Sensor Networks (VWSNs) [6, 7]. However, such visual monitoring capability imposes some challenges due to the resource-constrained nature of wireless sensors, since transmissions of visual data usually require higher communication bandwidth than transmissions of scalar data. In addition, source nodes need to process multimedia encoding algorithms and reliable communication is required for most VWSNs applications due to the lack of redundancy when video-based sensors are employed [8, 9]. As an example of how such a communication scenario can be challenging: while few bytes can represent monitored scalar information, such as humidity, pressure and temperature, a small uncompressed 128 \(\times\) 128 pixels 8-bit grayscale image is represented by 16,384 bytes (excluding the image header), demanding more bandwidth and potentially consuming more energy of intermediate nodes than scalar data. In such contexts, the resource-constrained hardware of modern wireless sensor motes [4] and the limited transmission bandwidth imposed by LR-WPAN communication technologies as IEEE 802.15.4 will render visual monitoring by still images a more suitable option for most video-based wireless sensor networks, since video transmission may rapidly deplete the energy resources of the nodes or be even unfeasible for transmissions from multiple sources through braided paths. The sensor networks where source nodes transmit still images and optionally some complementary scalar data can be referred to as Wireless Image Sensor Networks (WISNs). When transmitting image packets over error-prone wireless links, packets can be corrupted, and can require retransmission. In wireless image sensor networks, the lack of redundancy and the unique view of source sensors over the monitored field [9] turn the recovery of lost packets into a required service, since the desired view of a target may be only achieved by a unique source node. Additionally, some parts of the visual information transmitted from source nodes may be extremely relevant for the decoding process at the sink node. Data link technologies such as IEEE 802.15.4 retransmit corrupted packets, where correct reception is usually acknowledged in each link between intermediate nodes. As packet transmission consumes energy of the receiving and transmitting nodes, we can establish a direct relation between energy consumption and packet retransmission. In other words, when the packet error rate increases, more energy consumption is expected due to packet retransmission, but with very low or absent impact in the quality of the visual data received by the sink. While such behavior may be acceptable for traditional scalar sensor networks, some visual applications can afford loss in the image quality since the overall energy of the network is preserved. Camera-enabled source nodes can encode still images using Discrete Wavelet Transform (DWT). In short, DWT decomposes the original image in subbands with different relevancies for the reconstruction of the original image.