Formal analysis of subnet-based failure recovery algorithm in wireless sensor and actor and network

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Abstract

Wireless sensor and actor networks (WSANs) have various applications in safety and mission critical systems. Sensors are used for sensing the information whereas actors for taking intelligent decisions. Developing and modeling algorithms for WSANs have raised several research issues which have captured attention of the research community. Maintaining inter-actor connectivity or failure recovery is a critical issue in WSANs because these are deployed in harsh and inhospitable environment which may result into physical damage to actors loosing inter-actor connectivity. In case of failure of inter-actor connectivity, the topology of the network may be affected that might be inefficient to recover. Therefore an efficient subnet-based failure recovery algorithm (SFRA) is proposed in this work. It is assumed the partitioning of WSAN into subnets which localizes the failure recovery procedure at subnet level achieving objective of efficiency. Moreover, algorithm is hybrid as it assumes pre-failure planning and post-failure recovery. The proposed model is presented as a graph-based model to represent static part of the network topology. The graph model is transformed into a formal model using Vienna development method-specification language (VDM-SL). The static model is described by defining formal specification of subnets, network topology, sensors, actors and gateways as composite objects. The state space of the WSANs is described in the form of functions and operations as dynamic part of the model. Invariants are defined over the data types in static model for ensuring safety criteria and pre/post conditions are defined in functions and operations for changing state space of the system. The proposed model is validated and verified using VDM-SL Toolbox.

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Afzaal, H., & Zafar, N. A. (2016). Formal analysis of subnet-based failure recovery algorithm in wireless sensor and actor and network. Complex Adaptive Systems Modeling, 4(1). https://doi.org/10.1186/s40294-016-0037-4

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