Maintaining inter-actor connectivity is extremely crucial in mission-critical applications of Wireless Sensor and Actor Networks (WSANs), as actors have to quickly plan optimal coordinated responses to detected events. Failure of a critical actor partitions the inter-actor network into disjoint segments besides leaving a coverage hole, and thus hinders the network operation. This paper presents a Partitioning detection and Connectivity Restoration (PCR) algorithm to tolerate critical actor failure. As part of pre-failure planning, PCR determines critical/non-critical actors based on localized information and designates each critical node with an appropriate backup (preferably non-critical). The pre-designated backup detects the failure of its primary actor and initiates a post-failure recovery process that may involve coordinated multi-actor relocation. To prove the correctness, we construct a formal specification of PCR using Z notation. We model WSAN topology as a dynamic graph and transform PCR to corresponding formal specification using Z notation. Formal specification is analyzed and validated using the Z Eves tool. Moreover, we simulate the specification to quantitatively analyze the efficiency of PCR. Simulation results confirm the effectiveness of PCR and the results shown that it outperforms contemporary schemes found in the literature.

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Timely segregation of critical/noncritical nodes is extremely crucial in mobile ad hoc and sensor networks. Most of the existing segregation schemes are centralized and require maintaining network wide information, which may not be feasible in large-scale dynamic networks. Moreover, these schemes lack rigorous validation and entirely rely on simulations. We present a localized algorithm for segregation of critical/noncritical nodes (LASCNN) to the network connectivity. LASCNN establishes and maintains a k-hop connection list and marks a node as critical if its k-hop neighbours become disconnected without the node and noncritical otherwise. A noncritical node with more than one connection is marked as intermediate and leaf noncritical otherwise. We use both formal and nonformal techniques for verification and validation of functional and nonfunctional properties. First, we model MAHSN as a dynamic graph and transform LASCNN to equivalent formal specification using Z notation. After analysing and validating the specification through Z eves tool, we simulate LASCNN specification to quantitatively demonstrate its efficiency. Simulation experiments demonstrate that the performance of LASCNN is scalable and is quite competitive compared to centralized scheme with global information. The accuracy of LASCNN in determining critical nodes is 87% (1-hop) and 93% (2-hop) and of noncritical nodes the accuracy is 91% (1-hop) and 93% (2-hop).

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Wireless sensor and actor networks (WSAN) are captivating significant attention because of their suitability for safety-critical applications. Efficient actor placement in such applications is extremely desirable to perform effective and timely action across the deployment region. Nonetheless, harsh application environment inherently favors random placement of actors that leads to high concentration deployment and strangles coverage. Moreover, most of the published schemes lack rigorous validation and entirely rely on informal techniques (e.g., simulation) for evaluating nonfunctional properties of algorithms. This paper presents a localized movement control actor relocation (MCAR) algorithm that strives to improve connected coverage while minimizing movement overhead. MCAR pursues post-deployment actor repositioning in such a way that actors repel each other for better coverage while staying connected. We employ complementary formal and informal techniques for MCAR verification and validation. We model WSAN as a dynamic graph and transform MCAR to corresponding formal specification using Z notation. The resulting specification is analyzed and validated using Z eves tool. We simulate the specification to quantitatively demonstrate the efficiency of MCAR. Simulation results confirm the efficiency of MCAR in terms of movement overhead and connected coverage compared to contemporary schemes. The results show that MCAR can reduce distance movement up to 32 % while improving coverage up to 29 % compared to published schemes.