Reliability Analysis of Smart Home Sensor Systems Based on Probabilistic Model Checking

This study proposes a hierarchical probabilistic computation tree logic, HpCTL, which is an extension of the standard probabilistic computation tree logic pCTL, as a theoretical basis for hierarchical probabilistic CTL model checking. Hierarchical probabilistic model checking is a new paradigm that can appropriately verify hierarchical randomized (or stochastic) systems. Furthermore, a probability-measure-independent translation from HpCTL into pCTL is defined, and a theorem for embedding HpCTL into pCTL is proved using this translation. Finally, the relative decidability of HpCTL with respect to pCTL is proved using this embedding theorem. These embedding and relative decidability results allow us to reuse the standard pCTL-based probabilistic model checking algorithms to verify hierarchical randomized systems that can be described using HpCTL.

Probabilistic model checking has been developed for verifying systems that have stochastic and nondeterministic behavior. Given a probabilistic system, a probabilistic model checker takes a property and checks whether or not the property holds in that system. For this reason, probabilistic model checking provide rigorous guarantees. So far, however, probabilistic model checking has focused on propositional models where a state is represented by a symbol. On the other hand, it is commonly required to make relational abstractions in planning and reinforcement learning. Various frameworks handle relational domains, for instance, STRIPS planning and relational Markov Decision Processes. Using propositional model checking in relational settings requires one to ground the model, which leads to the well known state explosion problem and intractability. We present pCTL-REBEL, a lifted model checking approach for verifying pCTL properties of relational MDPs. It extends REBEL, a relational model-based reinforcement learning technique, toward relational pCTL model checking. PCTL-REBEL is lifted, which means that rather than grounding, the model exploits symmetries to reason about a group of objects as a whole at the relational level. Theoretically, we show that pCTL model checking is decidable for relational MDPs that have a possibly infinite domain, provided that the states have a bounded size. Practically, we contribute algorithms and an implementation of lifted relational model checking, and we show that the lifted approach improves the scalability of the model checking approach.

Smart homes are permeating our everyday lives. Different fast-evolving smart home technologies, various usage contexts, and heterogeneous users make it challenging to anticipate all requirements for smart homes in advance. User feedback gathered from the crowd of smart home technologies users is a valuable information source for verifying and eliciting requirements for system evolution. However, research on user feedback currently does not consider the smart home domain. Thus, we know only little about whether and how users of smart home technologies could provide feedback to support the elicitation of requirements and hence system evolution. In this paper, we present preliminary findings from our explorative interview study with smart home users on their attitudes towards feedback communication. Early results suggest that users are willing to provide feedback about their smart homes but have different needs regarding the feedback communication process. We assume the characteristics of smart home technologies (e.g., ubiquity, multimodality, connectivity) provide opportunities but also raise challenges for developing a user feedback approach in the context of smart homes.

With the rapid growth of smart home IoT devices, users are increasingly exposed to various security risks, as evident from recent studies. While seeking answers to know more on those security concerns, users are mostly left with their own discretion while going through various sources, such as online blogs, and technical manuals; which may render higher complexity to the regular users to extract the necessary information from. This requirement does not go along with the common mindsets of smart home users and hence threatens the security of smart homes furthermore. In this paper, we aim to identify and address the major user-level security concerns in smart homes. Specifically, we develop a novel dataset of Q&A from public forums, capturing practical security challenges faced by smart home users. We extract major security concerns in smart homes from our dataset by leveraging the Latent Dirichlet Allocation (LDA). We fine-tune relatively “smaller” transformer models, such as T5 and Flan-T5, on this dataset to build a QA system tailored for smart home security. Unlike larger models like GPT and Gemini, which are powerful but often resource hungry, and requiring data sharing, smaller models are more feasible for deployment in resource-constrained or privacy-sensitive environments, like smart homes. The dataset is manually curated and supplemented with synthetic data to explore its potential impact on model performance. This approach significantly improves the system’s ability to deliver accurate and relevant answers, helping users address common security concerns with smart home IoT devices. Our experiments on real-world user concerns show that our work improves the performance of the base models.

The increasing popularity of smart home devices has raised significant privacy issues. This project is motivated by two gaps in the literature. First, research has been focusing on smart home users' privacy perceptions rather than other stakeholders in a smart home considering various social factors. These social factors may include social relationships (e.g., guests and homeowners) and various social contexts (e.g., temporary residency). Second, most existing privacy mechanisms for smart homes are either proposed or implemented by researchers without users' input or designed solely for smart home users. This project takes the first attempt to fill these gaps.

The role of human behaviour in enterprise security is one of the little studied aspects. The author proposes a reinforcement model of collaborative security employing basic concepts from game theory, socio-psychology, and probabilistic model-checking. The proposed model aims towards solving the problem of inducing positive network effect to enable user centric monitoring of security violations, in particular, against violations related to ”semantic manipulation” of context dependent logical resources. Preventing such violations using existing security enforcement mechanisms is neither feasible nor cost effective. The author defines a payoff mechanism to formalize the model by stipulating appropriate payoffs as reward, punishment, and community price according to reporting of genuine or false violations, non-reporting of the detected violations, and proactive reporting of vulnerabilities and threats by the users. Correctness properties of the model are defined in terms of probabilistic robustness property and constraints for economic feasibility of the payoffs. For estimating the payoff parameters, system and user behaviours are further modelled in terms of probabilistic finite state machines (PFSM) and likelihood of the success of the model is specified using probabilistic computation tree logic (PCTL). PRISM model checker based automated quantitative analysis elicits the process of the estimation of various parameters in the model using PFSMs and PCTL formulas.

Port supply chains play a very important role in the process of economic globalization. Lack of trust of the mechanism is the main factor in restricting the development of port supply chains. Blockchains have great potential to solve the trust problem among all participants using port supply chains, which can reduce costs and improve efficiency. As the bridge between blockchains and port supply chains, smart contracts reconstruct the business process of blockchain-empowered port supply chains. In this article, we present an architecture of a consortium blockchain-empowered port supply chain system, and propose a system verification framework for the smart contracts of port supply chains with probabilistic behaviors. The smart contracts are modeled as DTMCs (Discrete-Time Markov Chains), which are automatically transformed through the BPMN (Business Process Model and Notation) description of the smart contracts. The requirements are specified by PCTL (Probabilistic Computation Tree Logic). Moreover, we implement the customs clearance process of the Shanghai Yangshan Port based on blockchain Hyperledger Fabric, and reconstruct the clearance process with smart contracts. We use it to demonstrate the effectiveness of this framework, and identify the smart contracts that do not meet the expected needs of users.

According to our survey about the express delivery from hundreds of campuses, we find that the location of commodity storage places impacts the delivery success rate and service quality. Thus, how to select the proper delivery location is vital to logistics enterprises, which contributes to improve the work efficiency and reduce delivery costs. In this paper, probabilistic model checking is used to verify the delivery fetch system, which evaluates the solution of location distributions in a quantitative way. First, it formalizes the fetch process of express delivery system between business and customer in the form of Discrete-Time Markov Chain (DTMC) when considering the stochastic behavior. Second, Probabilistic Computation Tree Logic (PCTL) is introduced as the verification property to the temporal behavior checking. Third, formal verifications are conducted by the supporting tool PRISM concerning on the transition probabilities computing. Furthermore, verification results are proven to follow the law of Bernouli Large Numbers, which aims to illustrate that the simulation result is close to the actual express delivery situation. Fourth, the express delivery location model is extended as new structure Co-DTMC in order to calculate time consumption and cost consumption, where the punishment factor is designed for the purpose of optimization. Finally, experiments are carried out to show that our approach can effectively select an optimal place for express delivery, which provides an alternative solution to integrate time and cost into the location selection.

Many traditional applications can be refined thanks to the development of blockchain technology. One of these services is non-repudiation, in which participants in a communication process cannot deny their involvement. Due to the vulnerabilities of the non-repudiation protocols, one of the parties involved in the communication can often avoid non-repudiation rules and obtain the expected information to the detriment of the interests of the other party, resulting in adverse effects. This paper studies the fairness guarantee quantitatively through probabilistic model checking. E-fairness is measured by modeling the protocol in probabilistic timed automata and verifying the appropriate property specified in the probabilistic computation tree logic. Furthermore, our analysis proposes insight for choosing suitable values for different parameters associated with the protocol so that a certain degree of fairness can be obtained. Therefore, the reverse question—for a certain degree of fairness ε, how can the protocol parameters be specified to ensure fairness—is answered.

SysML activity diagrams are OMG/INCOSE standard models for specifying and analyzing systems' behaviors. In this paper, we propose an abstraction approach for this type of diagrams that helps to mitigate the state-explosion problem in probabilistic model checking. To this end, we present two algorithms to reduce the size of a given SysML activity diagram. The first eliminates the irrelevant behaviors regarding the property under check, while the second merges control nodes into equivalent ones. The resulting abstracted model can answer safely the Probabilistic Computation Tree Logic (PCTL) property. Moreover, we present a novel calculus for activity diagrams (NuAC) that captures their underlying semantics. In addition, we prove the soundness of our approach by defining a probabilistic weak simulation relation between the semantics of the abstract and the concrete models. This relation is shown to preserve the satisfaction of the PCTL properties. Finally, we demonstrate the effectiveness of our approach on an online shopping system case study.

Model checking is an established method for verifying behavioral properties of system models. But model checkers tend to support low-level modeling languages that require intricate models to represent even the simplest systems. Modeling complexity arises in part from the need to encode domain knowledge at relatively low levels of abstraction. In this paper, we demonstrate that formalized domain knowledge can be reused to raise the abstraction level of model and property specifications, and hence the effectiveness of model checking. We describe a novel method for domain-specific model checking called cascading verification that uses composite reasoning over high-level system specifications and formalized domain knowledge to synthesize both low-level system models and their behavioral properties for verification. In particular, model builders use a high-level domain-specific language (DSL) based on YAML to express system specifications that can be verified with probabilistic model checking. Domain knowledge is encoded in the Web Ontology Language (OWL), the Semantic Web Rule Language (SWRL) and Prolog, which are used in combination to overcome their individual limitations. A compiler then synthesizes models and properties for verification by the probabilistic model checker PRISM. We illustrate cascading verification for the domain of uninhabited aerial vehicles (UAVs), for which we have constructed a prototype implementation. An evaluation of this prototype reveals nontrivial reductions in the size and complexity of input specifications compared to the artifacts synthesized for PRISM.

In formal verification, reliable results are of utmost importance. In model checking of digital systems, mainly incorrect implementations of the model checking algorithms due to logical errors are the source of wrong results. In probabilistic model checking, however, numerical instabilities are an additional source for inconsistent results. We motivate our investigations with an example, for which several state-of-the-art probabilistic model checking tools give completely wrong results due to inexact computations. We then analyze, at which points inaccuracies are introduced during the model checking process. We discuss first ideas how, in spite of these inaccuracies, reliable results can be obtained or at least the user be warned about potential correctness problems: (1) usage of exact (rational) arithmetic, (2) usage of interval arithmetic to obtain safe approximations of the actual probabilities, (3) provision of certificates which testify that the result is correct, and (4) integration of a "degree of belief" for each sub-formula into existing model checking tools.

Smart Home Technologies (SHTs) have recently become popular for a variety of purposes, including healthcare, entertainment, and convenience, among others. While SHT manufacturers promise to provide a range of services relating to home security, health and wellness, automated domestic tasks, entertainment, and beyond, user perceptions vary widely in terms of benefits and drawbacks. Moreover, surveillance studies researchers have warned against normalizing technologies that may exacerbate uneven power dynamics between users and household members, marketing companies, insurance brokers, law enforcement, and others. Through an analysis of the interdisciplinary literature stemming from computer science and engineering, gerontology, the social sciences, and related fields, we explore the extent to which these potential risks and related concerns are reflected upon by smart home users. This scoping review aims to explore SHT user perceptions of privacy attitudes, the purposes of smart home surveillance, risks and benefits, and impacts on home safety. Through our review of sixty-eight relevant studies, we found that many smart home users reported satisfaction over perceived benefits such as an increased sense of safety and home security. Many others displayed limited understandings of data collection practices or expressed privacy concerns. Nonetheless, SHT usage prevailed among these users. Others report a perceived trade-off between privacy and other factors, such as convenience, and some may have resorted to privacy cynicism, a coping mechanism for dealing with ubiquitous surveillance. In order to better understand SHT adoption trends despite concerns, exploring the conflict between user perceptions of privacy, understanding of SHT data collection purposes, risks and benefits, and home safety, is essential.

Internet-of-Things technology is being increasingly important in our daily lives. As IoT technology evolved, IoT devices face a data protection hazard, particularly smart home IoT gateway devices, which became evident. The demand for a low-cost, secure smart home gateway device or router among smart home users. The problem is that as the internet of things (IoT) becomes more ubiquitous, there is a growing need to simplify wireless network control mechanisms. Because data collecting and the process includes processes such as monitoring, judging, and controlling are all involved in IoT, the control mechanism is challenging to simplify. Many internets of things technology offer memory and communication capabilities, and are easily vulnerable to hacking, due to the mobile software available at the tip of one's fingers to operate the linked gadgets to the web. In the Internet of Things, secure data transfer is always a concern. To increase safety in IoT and wireless networks, the current study introduces a unique RSA-based method, as well as the AES algorithm and the lightweight protocol message queue telemetry transport (MQTT).

High-performance data streaming technologies are increasingly adopted in IT companies to support the integration of heterogeneous and possibly distributed applications. Compared with the traditional message queuing middleware, a streaming platform enables the implementation of event-streaming systems (ESS) which include not only complex queues but also pipelines that transform and react to the streams of data. By analysing the centralised data streams, one can evaluate the Quality-of-Service for other systems and components that produce or consume those streams. We consider the exploitation of <i>probabilistic model checking</i> as a performance monitoring technique for ESS systems. Probabilistic model checking is a mature, powerful verification technique with successful application in performance analysis. However, an ESS system may contain quantitative parameters that are determined by event streams observed in a certain period of time. In this paper, we present a novel theoretical framework called QV4M (meaning &#x201C;quantitative verification for monitoring&#x201D;) for monitoring ESS systems, which is based on two recent methods of probabilistic model checking. QV4M assumes the parameters in a probabilistic system model as random variables and infers the statistical significance for the probabilistic model checking output. We also present an empirical evaluation of computational time and data cost for QV4M.