## 2 Related Work
Moreover, it has not a general mechanism in order to accurately estimate the power consumption of the sensor nodes. In [29] a solution aiming to alleviate the requirement that not all TinyOS programs run on physical nodes is proposed, because that increases costs excessively while being inconvenient, so they allow only one physical node to exist which can be configured to be a neighbor of any virtual node. Thus, the potential faults which cannot be captured by pure simulation testing tools can be captured through the interaction between the physical node and other virtual nodes. Another advantage of their work is that the power consumption of a node in a large WSN can be estimated through the use of low-cost digital multimeters. Some considerations not taken under account on that work include the signal gains that currently are designated by the user and the fact that power consumption estimation may be improved. In [30] a hybrid simulation framework for WSN application development is proposed, that interconnects a virtual network with a physical network and then allows one to simulate the network as a whole. The authors created a novel model-based hybrid simulation framework for WSN applications, introducing the notion of hybrid simulation. They exploit HiL extensions in order to link some of the simulated WSN nodes with hardware-dependent features. Thus, by using Hy-Sim users can build a hybrid network consisting of virtual and real nodes and then simulate it as a whole. Actually, Hy-Sim supports the existence of purely virtual nodes, of purely physical nodes, but also of partially simulated nodes. The latter can have access to real sensors in order to complete part of their operations. Moreover, Hy-Sim allows nodes to have access to a real channel and even have access to data coming from real sensors. Although there have been contributions in the literature regarding the optimization of the wireless sensor networks from the point of view of planning and simulation technologies, there is still a shortage of methodologies and frameworks to carry out the in-field deployment, configuration and performance assessment of the overall system application. In [31], the authors proposed an embedded initialization and maintenance mechanism in which a flooding-based synchronization strategy is adopted to configure discovery and expiration time windows into which nodes can perform neighbor search and link assessment. Operational modes are triggered by dedicated points of the network, such as sink and root nodes, from which the mode changing process is realized. In order to enhance the flooding process and wake-up calls, techniques such as Trickle [32] are also considered. Another maintenance system to be embedded into the sensor nodes has been proposed in [33], which intends to provide maintenance services during the operational stage of the WSN by defining system coherency state policies. Such maintenance policies mainly rely on triggering conditions when encountering sensing failures and communication coverage failures, particularly considering protocol coherent states, which are organized as Global view of coherent states (system-wise, such as time synchronization requirements), and Local view of coherent state (node-wise, such as neighborhood awareness). The maintenance engine collects the coherent requirements and states, and then floods the list of protocols for the maintenance service to the network. Other works, such as the one proposed in [34], make use of Deployment Support Networks (DSN), which are wireless network temporarily installed alongside the actual WSN during the deployment process. The main target of this infrastructure is to provide two different radio communication capabilities, one for passive listening of network traffic and one for debugging channel connections through DSN nodes to distribute the gathered information to a nearby sink point, from which the deployment analysis will be performed.