## 1 Introduction  
### Background and Motivation  
Mobile Ad hoc Networks (MANETs) [1, 2] provide a versatile form of networking that enables the spontaneous formation of communication networks for data exchanges between peer mobile nodes without a need for dedicated infrastructure support. This versatility, apart from being valuable on its own sake, also blends well with the concept of the Internet of Things (IoT), which targets the aggregation of any networking-capable device under the assumption of common networking protocols [3]. IoT allows the connection of various physical things, called smart objects, to each other and to the Internet, through the use of heterogeneous communication technologies. The combination of the MANET and IoT concepts gives rise to the Future Internet paradigm of MANETs (named global MANET-IoT system [4]) that aims at the interoperability and interaction between heterogeneous networks (e.g., various types of MANETs and/or sensor networks [5]) and becomes attractive for developing a wide range of novel applications [6].  
A more specialized but highly relevant example aligning with the general principles of MANET is the Vehicle Ad hoc Network (VANET) paradigm [7]. This networking concept underlies Intelligent Transportation Systems [8] and is used to provide communication between nearby vehicles (i.e., vehicle to vehicle communication), and between vehicles and fixed infrastructure on the roadside (i.e., vehicle to infrastructure communication). In the special case where the vehicles are unmanned devices, the unmanned vehicle networks (UxVs) emerge [9; 10; 11; 12]. This type of network can be harnessed for military and civil applications, embracing a multitude of different environments. With respect to deployment, UxVs may target roads and ground terrains in general (UGVs), aerial applications (UAVs/Drones), the surface of maritime environments (USVs), and underwater applications (UUVs).  
The aforementioned scenarios present additional challenges when the involved MANETs are characterized by sparse topologies, i.e., when the mobile nodes encounter, on average, very few neighbor nodes within the communication range. In such networks, called Delay Tolerant Networks (DTNs) [13], mobile nodes may become temporarily disconnected and thus unable to immediately forward their messages to suitable next-hop nodes. To overcome this obstacle, the nodes must leverage their mobility and keep carrying the messages until they encounter forwarding opportunities (i.e., contacts with other suitable next-hop nodes). Accordingly, routing protocols for DTNs become more effective when the evaluation of the candidate next-hop nodes considers not only the current status of the nodes at the time of contact (i.e., their current position, possibly in conjunction with further attributes pertaining to the delay/disruption-tolerant application in hand), collectively called their \"forward effect/action\", but also their mobility pattern, determining their \"carry effect/action\", i.e., their suitability for effectively carrying the received message until encountering a better next-hop node.  
In view of the dynamic character and the volatility of the environments just described, there is a clear need for comprehensive methodologies to evaluate MANET-related technologies, applications, and protocols and to assess their effectiveness under diverse operational and environmental conditions. To this end, the most commonly employed techniques draw upon network simulation, network emulation, or testbed-based experimentation. (The terms \"testbed\" and \"platform\" are used interchangeably in the following.)