Trustworthiness Management in the Internet of Things

The future Internet of Things (IoT) will be characterized by an increasing number of object-to-object interactions for the implementation of distributed applications running in smart environments. Object cooperation allows us to develop complex applications in which each node contributes one or more services. The Social IoT (SIoT) is one of the possible paradigms that is proposed to make the objects’ interactions easier by facilitating the search for services and the management of objects’ trustworthiness. In that scenario, in which the information moves from a provider to a requester node in a peer-to-peer network, Trust Management Systems (TMSs) have been developed to prevent the manipulation of data by unauthorized entities and guarantee the detection of malicious behaviour. The cornerstone of any TMS is the ability to generate a coherent evaluation of the information received. The community concentrates effort on designing complex trust techniques to increase their effectiveness; however, strong assumptions still need to be considered. First, nodes could provide the wrong services due to malicious behaviours or malfunctions and insufficient accuracy. Second, the requester nodes usually cannot evaluate the received service perfectly. In this regard, this thesis proposes an exhaustive analysis of the trustworthiness management in the IoT and SIoT. To this, in the beginning, we generate a dataset and propose a query generation model, essential to develop a first trust management model to overcome all the attacks in the literature. So, we implement several trust mechanisms and identify the importance of the overlooked assumptions in different scenarios. Then, to solve the issues, we concentrate on the generation of feedback and propose different metrics to evaluate it based on the presence or not of errors. Finally, we focus on modelling the interaction between the two figures involved in interactions, i.e. the trustor and the trustee, and on proposing guidelines to efficiently design trust management models

A Binary Trust Game for the Internet of Things

The IoT is transforming the ordinary physical objects around us into an ecosystem of information that will enrich our lives. The key to this ecosystem is the cooperation among the devices, where things look for other things to provide composite services for the benefit of human beings. However, cooperation among nodes can only arise when nodes trust the information received by any other peer in the system. Previous efforts on trust were concentrated on proposing models and algorithms to manage the level of trustworthiness. In this paper, we focus on modelling the interaction between trustor and trustee in the IoT and on proposing guidelines to efficiently design trust management models. Simulations show the impacts of the proposed guidelines on a simple trust model

Can We Trust Trust Management Systems?

The Internet of Things is enriching our life with an ecosystem of interconnected devices. Object cooperation allows us to develop complex applications in which each node contributes one or more services. Therefore, the information moves from a provider to a requester node in a peer-to-peer network. In that scenario, trust management systems (TMSs) have been developed to prevent the manipulation of data by unauthorized entities and guarantee the detection of malicious behaviour. The community concentrates effort on designing complex trust techniques to increase their effectiveness; however, two strong assumptions have been overlooked. First, nodes could provide the wrong services due to malicious behaviours or malfunctions and insufficient accuracy. Second, the requester nodes usually cannot evaluate the received service perfectly. For this reason, a trust system should distinguish attackers from objects with poor performance and consider service evaluation errors. Simulation results prove that advanced trust algorithms are unnecessary for such scenarios with these deficiencies

A Channel Selection Model based on Trust Metrics for Wireless Communications

Dynamic allocation of frequency resources to nodes in a wireless communication network is a well-known method adopted to mitigate potential interference, both unintentional and malicious. Various selection approaches have been adopted in literature, to limit the impact of interference and keep a high quality of wireless links. In this paper, we propose a different channel selection method, based on trust policies. The trust management approach proposed in this work relies on the node’s own experience and trust recommendations provided by its neighbourhood. By means of simulation results in Network Simulator NS-3, we demonstrate the effectiveness of the proposed trust method, while the system is under jamming attacks, in respect of a baseline approach. We also consider and evaluate the resilience of our approach in respect of malicious nodes, providing false information regarding the quality of the channel, to induct bad channel selection of the node. Results show how the system is resilient in respect of malicious nodes, keeping around 10% of throughput more than an approach only based on the own proper experience, considering the presence of 40% of malicious nodes, both single and collusive attacks

Analysis of Feedback Evaluation for Trust Management Models in the Internet of Things

The Internet of Things (IoT) is transforming the world into an ecosystem of objects that communicate with each other to enrich our lives. The devices’ collaboration allows the creation of complex applications, where each object can provide one or more services needed for global benefit. The information moves to nodes in a peer-to-peer network, in which the concept of trustworthiness is essential. Trust and Reputation Models (TRMs) are developed with the goal of guaranteeing that actions taken by entities in a system reflect their trustworthiness values and to prevent these values from being manipulated by malicious entities. The cornerstone of any TRM is the ability to generate a coherent evaluation of the information received. Indeed, the feedback generated by the consumers of the services has a vital role as the source of any trust model. In this paper, we focus on the generation of the feedback and propose different metrics to evaluate it. Moreover, we illustrate a new collusive attack that influences the evaluation of the received services. Simulations with a real IoT dataset show the importance of feedback generation and the impact of the new proposed attack

Fertilization induces a transient exposure of phosphatidylserine in mouse eggs

Phosphatidylserine (PS) is normally localized to the inner leaflet of the plasma membrane and the requirement of PS translocation to the outer leaflet in cellular processes other than apoptosis has been demonstrated recently. In this work we investigated the occurrence of PS mobilization in mouse eggs, which express flippase Atp8a1 and scramblases Plscr1 and 3, as determined by RT-PCR; these enzyme are responsible for PS distribution in cell membranes. We find a dramatic increase in binding of flouresceinated-Annexin-V, which specifically binds to PS, following fertilization or parthenogenetic activation induced by SrCl2 treatment. This increase was not observed when eggs were first treated with BAPTA-AM, indicating that an increase in intracellular Ca2+ concentration was required for PS exposure. Fluorescence was observed over the entire egg surface with the exception of the regions overlying the meiotic spindle and sperm entry site. PS exposure was also observed in activated eggs obtained from CaMKIIγ null females, which are unable to exit metaphase II arrest despite displaying Ca2+ spikes. In contrast, PS exposure was not observed in TPEN-activated eggs, which exit metaphase II arrest in the absence of Ca2+ release. PS exposure was also observed when eggs were activated with ethanol but not with a Ca2+ ionophore, suggesting that the Ca2+ source and concentration are relevant for PS exposure. Last, treatment with cytochalasin D, which disrupts microfilaments, or jasplakinolide, which stabilizes microfilaments, prior to egg activation showed that PS externalization is an actin-dependent process. Thus, the Ca2+ rise during egg activation results in a transient exposure of PS in fertilized eggs that is not associated with apoptosis.Fil: Curia, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental (i); ArgentinaFil: Ernesto, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental (i); ArgentinaFil: Stein, Paula. University of Pennsylvania; Estados UnidosFil: Busso, Dolores. Pontificia Universidad Católica de Chile; ChileFil: Schultz, Richard. University of Pennsylvania; Estados UnidosFil: Cuasnicu, Patricia Sara. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental (i); ArgentinaFil: Cohen, Debora Juana. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental (i); Argentin

The Role of Attitudes Toward Medication and Treatment Adherence in the Clinical Response to LAIs: Findings From the STAR Network Depot Study

Background: Long-acting injectable (LAI) antipsychotics are efficacious in managing psychotic symptoms in people affected by severe mental disorders, such as schizophrenia and bipolar disorder. The present study aimed to investigate whether attitude toward treatment and treatment adherence represent predictors of symptoms changes over time. Methods: The STAR Network \u201cDepot Study\u201d was a naturalistic, multicenter, observational, prospective study that enrolled people initiating a LAI without restrictions on diagnosis, clinical severity or setting. Participants from 32 Italian centers were assessed at three time points: baseline, 6-month, and 12-month follow-up. Psychopathological symptoms, attitude toward medication and treatment adherence were measured using the Brief Psychiatric Rating Scale (BPRS), the Drug Attitude Inventory (DAI-10) and the Kemp's 7-point scale, respectively. Linear mixed-effects models were used to evaluate whether attitude toward medication and treatment adherence independently predicted symptoms changes over time. Analyses were conducted on the overall sample and then stratified according to the baseline severity (BPRS < 41 or BPRS 65 41). Results: We included 461 participants of which 276 were males. The majority of participants had received a primary diagnosis of a schizophrenia spectrum disorder (71.80%) and initiated a treatment with a second-generation LAI (69.63%). BPRS, DAI-10, and Kemp's scale scores improved over time. Six linear regressions\u2014conducted considering the outcome and predictors at baseline, 6-month, and 12-month follow-up independently\u2014showed that both DAI-10 and Kemp's scale negatively associated with BPRS scores at the three considered time points. Linear mixed-effects models conducted on the overall sample did not show any significant association between attitude toward medication or treatment adherence and changes in psychiatric symptoms over time. However, after stratification according to baseline severity, we found that both DAI-10 and Kemp's scale negatively predicted changes in BPRS scores at 12-month follow-up regardless of baseline severity. The association at 6-month follow-up was confirmed only in the group with moderate or severe symptoms at baseline. Conclusion: Our findings corroborate the importance of improving the quality of relationship between clinicians and patients. Shared decision making and thorough discussions about benefits and side effects may improve the outcome in patients with severe mental disorders

Can We Trust Trust Management Systems?

The Internet of Things is enriching our life with an ecosystem of interconnected devices. Object cooperation allows us to develop complex applications in which each node contributes one or more services. Therefore, the information moves from a provider to a requester node in a peer-to-peer network. In that scenario, trust management systems (TMSs) have been developed to prevent the manipulation of data by unauthorized entities and guarantee the detection of malicious behaviour. The community concentrates effort on designing complex trust techniques to increase their effectiveness; however, two strong assumptions have been overlooked. First, nodes could provide the wrong services due to malicious behaviours or malfunctions and insufficient accuracy. Second, the requester nodes usually cannot evaluate the received service perfectly. For this reason, a trust system should distinguish attackers from objects with poor performance and consider service evaluation errors. Simulation results prove that advanced trust algorithms are unnecessary for such scenarios with these deficiencies

A Dataset for Performance Analysis of the Social Internet of Things

Node, service and information discovery as well as trust management are key issues that characterize the IoT when huge numbers of nodes have to collaborate to support the deployed applications. A recent promising proposal, with the ability to address these issues, is the Social IoT (SIoT) paradigm, whose main principle is to enable objects to autonomously establish social links with to each other (adhering to rules set by their owners). To be able to test and validate this ability, significant datasets regarding objects' networks (node description, typology, activities, exchanged traffic) are needed, which however are not completely available. This paper addresses this issue by presenting a dataset that has been realized on the basis of real IoT objects available in the city of Santander and categorized following the typologies and data model for objects introduced in the FIWARE Data Models. Object profiles and guidelines for the relationships' creation for the SIoT are described and the obtained data and the resulting social network is made available to the research community

Energy efficiency in smart building: A comfort aware approach based on Social Internet of Things

Efficient energy usage inside buildings is a critical problem, particularly for high loads such as the Heating, Ventilation and Air Conditioning (HVAC) systems: over 30% of the global energy consumption resides in HVAC usage inside buildings. Usage awareness and efficient management of HVAC have the potential to significantly reduce related costs. Nevertheless, strict saving policies may contrast with users' comfort. This paper proposes a Smart HVAC system where a trade-off between energy costs and thermal comfort of users is achieved. This user-centric approach takes advantage of the Social Internet of Things (SIoT) paradigm to augment real world objects with a virtual counterpart that leverages social consciousness to interact with other objects. Accordingly, a building thermal profile is characterized to drive the selection of the most appropriate working times for the HVAC. Experimental results prove that the implemented system is able to adapt to user's needs and ensure an acceptable comfort level while at the same time reducing energy costs with reference to static or traditional scenarios