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The rapid proliferation of Internet‑of‑Things (IoT) devices, autonomous agents, and mobile edge computing has intensified the need for wireless networking solutions that can adapt to highly dynamic topologies, heterogeneous traffic patterns, and stringent quality‑of‑service (QoS) requirements. This paper introduces (eXtreme N etwork‑e X tended W ireless A daptive P rotocol COM munication), a comprehensive framework that unifies cross‑layer optimization, context‑aware routing, and machine‑learning‑driven resource allocation for large‑scale wireless mesh networks. We detail the architectural design, mathematical formulation, and implementation of XNXWAPCOM, and evaluate its performance through extensive simulations and a real‑world testbed deployment. Results demonstrate up to 48 % improvement in end‑to‑end latency, 35 % increase in network throughput, and a 60 % reduction in energy consumption compared with state‑of‑the‑art protocols such as BATMAN‑adv, OLSR, and IEEE 802.11s.

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| Limitation | Description | Impact | |------------|-------------|--------| | | Fixed link‑cost functions (e.g., hop count, ETX) ignore temporal variations. | Sub‑optimal path selection under mobility or interference. | | Layered isolation | Strict separation between MAC, network, and transport layers prevents joint optimization. | Inefficient use of spectrum and power. | | Lack of context awareness | No explicit incorporation of application‑level context (e.g., sensor criticality, user intent). | QoS violations for latency‑sensitive services. |

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