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Bio-inspired Networks and Systems

Computation and information underlie all technology progress, but they are also intrinsic components of other diverse fields ranging from biology to physics or from society to economics. In fact, structures providing communication, storage, and processing of information can be found not only in hardware devices and software programs, but also in nature and society as biological and social networks. The focus of our research line falls within the realm of these cross-disciplinary domain which aim to create novel and efficient networking paradigms inspired by both engineered and natural communication systems.

Over the last years this research group has been working on engineered systems, mainly for wireless-optical access networks where wireless routers at the front end are scattered over an area to provide Internet access to clients connected to them. Now we intend to extend our research at the wireless section to wireless sensor networks (WSNs) which are having a tremendous growth and are prone to incorporating natural methods.




These networks are composed of smart sensing wireless nodes, interconnected in an adhoc or structured way, and able to capture data from the environment. By combining proper data with adequate processing and communication, these network systems can be used for many purposes, such as traffic monitoring, fire detection, agricultural monitoring, industrial control, home automation, giving rise to smart cities and ambient intelligence.

We will explore dynamic oriented and bio-inspired approaches towards particular problems in WSNs which may include game theory, where conflict and cooperation between intelligent rational decision-makers are modeled mathematically, or machine learning and multi-agent systems, where interaction between autonomous individuals and learning from data is the focus. Mathematical optimization also plays a major role for planning, performance improvement and analysis of impact. Bio-inspired solutions to problems related to computing and networking can be found either from the bio-inspired computing application domain, comprising algorithms for efficient computing and pattern recognition, or from the bio-inspired networking application domain, where strategies for efficient and scalable networking under uncertain conditions are on focus.
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