Multi-Criteria Methodology for Selecting Communication Protocols in M2M Environments

The continuous expansion of Machine-to-Machine (M2M) communication and Internet of Things (IoT) ecosystems has significantly increased the complexity of selecting appropriate communication protocols and data flow management systems. Contemporary M2M deployments operate across heterogeneous functional domains, including sensor networks, transactional systems, and real-time streaming environments, each imposing distinct and often conflicting non-functional requirements such as latency, reliability, scalability, and resource efficiency. This study proposes a domain-oriented multi-criteria decision-making methodology for structured protocol selection in M2M environments. The framework integrates the Analytic Hierarchy Process (AHP) for context-dependent weighting of evaluation criteria with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for quantitative ranking of alternative technologies. A structured domain taxonomy is introduced to dynamically align evaluation priorities with functional deployment characteristics, and 99th percentile latency (Lp99) is incorporated as a primary performance indicator to capture tail-behavior effects critical for M2M reliability. Beyond ranking computation, the methodology formalizes a reproducible analytical workflow linking empirical measurements to domain-specific decision out-comes and incorporates a sensitivity-analysis perspective to assess ranking robustness under variations in criterion weights. The proposed framework establishes a transparent and adaptable decision-theoretic foundation for context-aware communication protocol selection in heterogeneous M2M scenarios.