Automating Conflict Detection and Mitigation in Large-Scale IoT Systems

Pradeep, Pavana; Pal, Amitangshu; Kant, Krishna

doi:10.1109/ccgrid51090.2021.00063

Cited by 6 publications

(8 citation statements)

References 16 publications

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“…However, a single time slot is inadequate since the activities can occur over a wide range of time scales. We address this Pavana et al [18] by defining time-slots of a few different durations. The extended SMT model can be solved using the NuXMV model checker proposed in Cavada et al [93].…”

Section: Proactive Conflict Detectionmentioning

confidence: 99%

“…Another piece of work is performed in the context of static and dynamic conflicts in Pavana, et al [18]. The authors identified two distinct types of conflicts that can occur in a static or dynamic environment and developed an intelligent rule perturbation model to mitigate each type of conflict: (1) direct conflict that occurs for some action A, if there are two rules C 1 =⇒ A, and C 2 =⇒ ¬A, and both conditions C 1 and C 2 are simultaneously true; (2) extended conflicts that occur when a safety property is violated.…”

Section: Literature Analysis Of Conflict Characterizationmentioning

confidence: 99%

“…Dynamic analysis approaches shown in Pavana et al [18], Fernandes et al [81], Jia et al [88], Wang et al [89], and Babun et al [90] do not have the drawbacks associated with static analysis. As stated in Ernst et al [91], static analysis may result in over-approximations by generalizing all possible behaviors of an application from its source code, potentially resulting in false positives.…”

Section: Dynamic Analysis Of Iot Systemsmentioning

confidence: 99%

“…As discussed earlier, proactive conflict detection and corresponding resolution can ideally combine the best aspects of static and dynamic methods, i.e., accurate dynamic avoidance of conflicts that are likely to occur in the near future. Pavana et al [18] devised an approach that not only detects conflicts proactively, but also resolves them by an automated loosening of rules rather than by simply blocking an action. We describe this next briefly; we are not aware of other proactive approaches.…”

Section: Proactive Conflict Resolution In Iot Systemsmentioning

confidence: 99%

“…For conflict resolution, in Pavana et al [18], we considered the approach of perturbing the ORs minimally to eliminate the conflict. Often, the ORs involve some thresholds that can be tweaked without really changing their nature.…”

Section: Automated Conflict Resolutionmentioning

confidence: 99%

See 4 more Smart Citations

Conflict Detection and Resolution in IoT Systems: A Survey

Pradeep

Kant

2022

IoT

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Internet of Things (IoT) systems are becoming ubiquitous in various cyber–physical infrastructures, including buildings, vehicular traffic, goods transport and delivery, manufacturing, health care, urban farming, etc. Often multiple such IoT subsystems are deployed in the same physical area and designed, deployed, maintained, and perhaps even operated by different vendors or organizations (or “parties”). The collective operational behavior of multiple IoT subsystems can be characterized via (1) a set of operational rules and required safety properties and (2) a collection of IoT-based services or applications that interact with one another and share concurrent access to the devices. In both cases, this collective behavior often leads to situations where their operation may conflict, and the conflict resolution becomes complex due to lack of visibility into or understanding of the cross-subsystem interactions and inability to do cross-subsystem actuations. This article addresses the fundamental problem of detecting and resolving safety property violations. We detail the inherent complexities of the problem, survey the work already performed, and layout the future challenges. We also highlight the significance of detecting/resolving conflicts proactively, i.e., dynamically but with a look-ahead into the future based on the context.

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Section: Proactive Conflict Detectionmentioning

confidence: 99%

Section: Literature Analysis Of Conflict Characterizationmentioning

confidence: 99%

Section: Dynamic Analysis Of Iot Systemsmentioning

confidence: 99%

Section: Proactive Conflict Resolution In Iot Systemsmentioning

confidence: 99%

Section: Automated Conflict Resolutionmentioning

confidence: 99%

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Conflict Detection and Resolution in IoT Systems: A Survey

Pradeep

Kant

2022

IoT

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Attention-Driven Conflict Management in Smart IoT-Based Systems

Awanyo,

Guermouche

2024

Lecture Notes in Computer Science

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Intelligent conflict detection of IoT services using high-level Petri nets

Yang,

Wu,

Gui

et al. 2024

Complex Intell. Syst.

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Internet of Things has become a common paradigm for various domains. To meet a user’s complex requirement, we should compose multiple IoT devices (IoT services) to provide comprehensive services to the user. However, these services usually coexist, which is likely to lead to conflicts. Moreover, each user may have different kinds of needs. Suppose that in a smart home there are more than one person, a conflict may occur when they request the same service in this environment. Actually, even though they request different IoT services, among which if there exists function impact or QoS impact, a conflict could still occur. In this paper, we propose to employ high-level Petri nets to detect conflicts among IoT services. We first model the formal methods for conflict policies. Then, we present a Petri nets-based mechanism for modeling and detecting conflicts. Finally, we expand the previous model and use high-level Petri nets for handling fuzzy IoT conflict policies modeling and reasoning. The experimental results show that the proposed approach performs well in accuracy.

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Automating Conflict Detection and Mitigation in Large-Scale IoT Systems

Cited by 6 publications

References 16 publications

Conflict Detection and Resolution in IoT Systems: A Survey

Conflict Detection and Resolution in IoT Systems: A Survey

Attention-Driven Conflict Management in Smart IoT-Based Systems

Intelligent conflict detection of IoT services using high-level Petri nets

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