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AfE-EA's Problem Assessment

AfE-EA is the abbreviation of LPWAN-AfE-EA: Low Power Wireless Area Network – All for Everyone & Energy Aware Communication Network Protocol and was inspired in the ability of all the devices/sensors of an instance/ecosystem, e.g. a wireless sensor network (WSN) of an Utility Infrastructure, can communicate efficiently with each others.  

In wireless sensor networks (WSN), the network infrastructure performance depends heavily on the efficiency of the communications network routing protocol.

The communication network routing protocol must deal efficiently with challenges such as energy constraints, low storage capacity, low bandwidth, low processing capability, device/sensor location sparsity, fault tolerance, among others.

All of these factors can affect the quality of service (QoS) of the automated metering infrastructure (AMI), or similar.  

Having those challenges in mind, was developed the AfE-EA, a communication network routing protocol that allows for battery-operated or energy-constrained devices, even in the case of powering by energy harvesting, to form a proper meshed/ad-hoc communications network by implementing new disruptive features for efficient routing and managing of the energy consumption/availability of all devices/sensors of the infrastructure.

AfE-EA was developed to have the same common architecture of the power smart grids AMI infrastructures, composed of a control center (CC), data concentrators (DC) and groups of devices/sensors. The DC is responsible for gathering all the data from the devices/sensors and also managing all the communication network routing. The DC is the device responsible to compute the all the optimal communication network routings, from the DC to all the devices/sensors and vice-versa.   

AfE-EA is based in an efficient time complexity algorithm to compute optimal hop-constrained maximum capacity spanning trees, to be used in bidirectional communication networks, even in the case of battery-operated or energy-constrained devices/sensors that can operate together mains power supplies operated devices/sensors.

The general principle followed was that the communication routing protocol should compute periodically or, when necessary, the optimal network spanning trees in order to use preferably as routers the devices/sensors with more available energy and with optimal hop-constraints and, this way, adjust the available energy of the whole network’s devices/sensors by performing a balanced consumption in all the devices/sensors, in order to maximize the period of operation of the whole infrastructure network. 

The bottom line is that since AfE-EA computational complexity is fairly low, it is possible to compute optimal network hop-constrained spanning trees in real time, which is of major importance to cope with the needs of a self-healing/rearrangement routing. In case of battery-operated or energy-constrained devices, this functionality also allows the achieving of a network lifetime that is in line with the infrastructure's hardware lifetime.

As well known, in accordance with the current science state-of-the-art, the computation of even only hop-constrained minimum spanning trees (H-CMST) of dozens of devices/sensors is a math problem of class NP-Hard of high computational complexity, which cannot be performed efficiently in polynomial time by the current IT resources / computers available nowadays, even for the most sophisticated. 

Therefore, one of the biggest outcomes is directly related with AfE-EA's computational efficiency that enables the possibility to use very small and low-cost processors, with low resources and battery-operated or energy-constrained, even in the case of be powered by energy harvesting. 

AfE-EA also enables the possibility of having fully automated managing systems (AMS), namely in automated metering infrastructures (AMI), allowing the user to have very large networks without worrying about the communication network itself but only about processing the data that is continuously gathered. 

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