# Distributed Localization with Complemented RSS and AOA Measurements: Theory and Methods
## 5 Summary and Future Prospects  
This survey studied the problem of distributed target localization based on amalgamated RSS and AOA measurements. It provided formal mathematical derivation of the problem and presented a set of existing solutions. These state-of-the-art methods were studied from a signal processing perspective and their basic ideas were elaborated without focusing on extensive technical details characteristic to each one of the approaches. An analysis from both computational complexity and localization accuracy point of view was provided in order to give an intuition to the reader about the performance of the existing approaches. Likewise, the most commonly used theoretical lower bound (in the literature) on the localization accuracy was reviewed, as well, with the objective to evaluate the margin for further improvements. Some of the described approaches in this survey are computationally demanding and some require installing additional hardware (e.g., antenna arrays or directional antennas) on all nodes in the network. The former issue might restrict the applicability of such approaches in real-time or in resource-restrained networks (e.g., battery lives of nodes), at least for the time being. The latter issue raises the question of financial pay off for using such approaches in a wide variety of applications (e.g., in military or fire prevention applications where nodes are deployed in areas where they are likely to be damaged or destroyed). Both issues are equally important and should not be disregarded in the development of future solutions for target localization in general. The application in fire detection/prevention is very important in countries where the climate favors this hazard, such as Portugal. In the last 20 years, Portugal has been severely affected by large wildfires with dramatic consequences and casualties. Hence, it is urgent that the scientific community provides sound and efficient tools capable of improving decision making during wildfires crisis to minimize its negative consequences. A key component in decision support mechanisms for operational interventions is the information obtained within the network, which might be useless without knowing the location of nodes that acquired the information. Currently, the hardware used in forest surveillance is based on expensive video cameras installed on tall towers, complemented by meteorological sensors. There is no operational decision support system functioning in real-time fed by data provided by the  
Figure 14: ARMSE (m) versus \(R\) (m): \(N=15\), \(M=50\), \(M_{c}=1000\). network, mostly because it requires significant resources (human and equipment), which are very limited during wildfires. In the following years, we expect to see a further boost in the research interest for target localization in general. Our reasoning is founded on the fact that we are living in a period of incessant and rapid development of novel technologies and introduction of new applications. For instance, employing localization technologies to complement and enhance vehicular and pedestrian systems in the fields of intelligent transportation systems, location-based services, robotics, and automated vehicles has already began. Similarly with the line of research presented here, a huge potential for future improvements of localization performance is expected to emerge from a fusion of information coming from already deployed (or novel) infrastructures (e.g., signals of opportunity), as well as the use of crowd sensing, owing to the widespread use of smartphones today. The forthcoming technologies (5G and IoT) will continue attracting the research interest towards distributed target localization without a doubt. It is expected that the IoT will continue nonwishing the growth of networks in their size and their dynamics. Due to the augmented network size and node mobility, implementation of localization algorithms in a distributed manner will become a requirement for many practical settings, like smart buildings and smart cities.