We present SenseLess, a hybrid anomaly detection framework for smart homes that, during the training phase, automatically labels images without manual annotation by combining sensor-guided detection, self-supervised visual clustering, and unsupervised multi-sensor delay estimation for precise alignment. During operation, the system relies primarily on non-vision sensors and activates a confidence-aware vision model only under low-confidence, thereby preserving privacy while maintaining adaptability. Evaluated in real home monitoring, SenseLess achieved an average label coverage of 97.65% with 94.9% accuracy and reduced vision usage to less than 4% of wall-clock operating time. Calibration mechanisms and minimal configuration requirements support scalability and deployment across diverse residential environments.

Abstract Smart buildings remain heterogeneous across sensing infrastructure, metadata quality, legacy protocols, and analytics requirements, hindering reusable human–building natural language interfaces. We present OntoSage , a modular framework for ontologically grounded question answering (QA) and fulfillment of analytic intents over smart building data. The framework (i) leverages Brick Schema-based RDF model with reasoning capabilities, (ii) translates natural language (NL) questions into executable SPARQL via a fine-tuned seq2seq model (T5-Base), and (iii) orchestrates portable analytics microservices that operate on time-series sensor data referenced through ontology-linked UUIDs. A summarization component (open-weights Mistral-7B, zero-shot) converts structured SPARQL/SQL/analytic outputs into concise stakeholder-aware responses without requiring task-specific fine-tuning. We categorize QA complexity into four reasoning classes and report component-level execution metrics supporting these categories. To address portability, we formalize a lightweight adaptation workflow (ontology ingestion $$\rightarrow $$ entity enrichment for NLU $$\rightarrow $$ NL2SPARQL validity checks $$\rightarrow $$ analytics binding) designed to minimize per-building retraining. Reproducibility is enabled through public source code, synthetic and ontology-derived datasets, Docker/Compose service descriptors, and documented supporting scripts “( https://github.com/suhasdevmane/OntoBot )”. The developers’ documentation is publicly accessible “( https://ontosage-docs.github.io )”.

Biodiversity conservation in fragmented and remote ecosystems often requires labour-intensive, time-consuming fieldwork, placing staff at risk and limiting the scope of long-term monitoring. To address these challenges in a sustainable, locally adaptable manner, we present BearWave, a novel, place-based HF communication framework attuned to local infrastructure constraints and designed to support low-power sensing networks under severe radio-frequency (RF) conditions. By leveraging Near Vertical Incidence Skywave (NVIS) propagation and the FT8 digital modulation technique, BearWave achieves reliable, bidirectional data transfer—even in dense tropical rainforest conditions—while keeping costs under £200 per node and enabling extended battery-powered operation. A case study in UK woodlands, chosen as an environmental analogue to Borneo’s rainforest, demonstrated BearWave’s robustness and adaptability: despite dense vegetation and non-line-of-sight paths, the system maintained over 90% message reliability at distances of up to 25 km using only 1 W of RF power. Notably, the strongest signal propagation and best reception rates occurred during nighttime, reflecting diurnal ionospheric variations. These empirical results confirm that BearWave outperforms conventional technologies such as LoRaWAN or satellite systems in harsh, attenuating environments, offering a scalable, energy-efficient approach that lowers both ecological footprints and staff labor risks. This research advances conservation-focused communication by providing a universal, scientifically validated framework capable of supporting long-term ecological monitoring, poacher detection, and improved animal welfare. Crucially, BearWave’s low-cost, low-impact design broadens access for under-resourced organisations and community-driven conservation programs, where local knowledge and stakeholder insights help shape technology decisions on the ground. By embracing a socio-technical innovation model, BearWave exemplifies how computing can be sustainably embedded in remote ecosystems worldwide.

Adaptive behavior plays a critical role in how individuals navigate discomfort, influencing their ability to respond to environmental challenges. Addressing thermal discomfort which is a pressing concern in the context of climate adaptation and sustainable living. It requires designers and developers to move beyond traditional Graphical User Interface (GUI) solutions, embracing interactive and physically engaging design approaches. This paper introduces D-FACT (Discomfort Card-Based Toolkit for Facilitating Adaptation), a participatory design tool developed to foster sustainable and inclusive adaptation strategies. Tested in four collaborative workshops with 40 participants, D-FACT enables both designers and non-designers to ideate solutions for adapting to thermal discomfort. Our findings demonstrate the toolkit’s effectiveness in nurturing creative, cooperative approaches that promote adaptive environments while advancing energy efficiency and environmental health. By integrating principles of sustainable design, the toolkit encourages diverse and inclusive participation, resulting in conceptual designs that address localized challenges in thermal adaptation. This work contributes to the discourse on computing and sustainability, offering practical methods for embedding participatory design into efforts to create resilient, equitable, and resource-efficient spaces.

The integration of Internet of Things (IoT) devices in industrial applications has become viable due to advancements in ubiquitous computing that enable complex machine learning (ML) tasks on resource-constrained devices. Unlike prior approaches that rely on built-in sensors, our system utilizes externally gathered Inertial Measurement Units (IMU) data for anomaly detection. In this paper, we show that simple 1D-CNN and LSTM models on an ultra-low-power device (Nicla Sense ME) optimized for edge-based industrial anomaly detection can achieve approximately 98 movement-based anomalies (e.g., collisions and joint velocity deviations) in industrial robotic arms. We analyzed an advanced manufacturing scenario where the robotic arm performs three consecutive, distinct tasks (pick-and-place, painting, and screwdriving) and demonstrated that the proposed anomaly detection system is task-independent. We implemented these models ondevice by designing a minimal model architecture and modifying source code to minimize RAM usage and Bluetooth Low Energy (BLE) overhead. Additionally, we examined the challenges of deploying ML models in resource-constrained environments by analyzing various quantization methods and the impact of hyperparameter choices on inference time, accuracy, and memory consumption. Our approach focuses on detecting anomalies directly at the data source which enables true real-time detection with a complete edge computing framework that achieves a 10Hz data frequency and a 250ms inference time when BLE is active. Furthermore, we generated a comprehensive dataset capturing quaternion and IMU data from an industrial robotic arm over 26 hours, including various anomaly scenarios, and made the source code available on GitHub for replicability.

People are increasingly bringing Internet of Things (IoT) devices into their homes without understanding how their data is gathered, processed, and used. We describe PrivacyCube, a novel data physicalization designed to increase privacy awareness within smart home environments. PrivacyCube visualizes IoT data consumption by displaying privacy-related notices. PrivacyCube aims at assisting smart home occupants to (i) understand their data privacy better and (ii) have conversations around data management practices of IoT devices used within their homes. Using PrivacyCube, households can learn and make informed privacy decisions collectively. To evaluate PrivacyCube, we used multiple research methods throughout the different stages of design. We first conducted a focus group study in two stages with six participants to compare PrivacyCube to text and state-of-the-art privacy policies. We then deployed PrivacyCube in a 14-day-long in-home field study with eight households. Lastly, we conducted an event-based field study comparing PrivacyCube with a mobile application, engaging 26 participants with diverse demographics. Our results show that PrivacyCube helps home occupants comprehend IoT privacy better with significantly increased privacy awareness at p < .05 (p = 0.00041, t = -5.57). Participants preferred PrivacyCube over text privacy policies because it was comprehensive and easier to use. PrivacyCube, Privacy Label, and the mobile application, all received positive reviews from participants, with PrivacyCube being preferred for its interactivity and ability to encourage conversations. PrivacyCube was also considered by home occupants as a piece of home furniture , encouraging them to socialize and discuss IoT privacy implications using this device. Watch the demo ( Demo Video ) ( Source Code ).

Stress has emerged and continues to be a regular obstacle in people's lives. When left ignored and untreated, it can lead to many health complications, including an increased risk of death. In this study, we propose a foundation model approach for stress detection without the need to train the model from scratch. Specifically, we utilise the foundation model "Neuro-GPT", which was trained on a large open dataset (TUH EEG) with 20,000 EEG recordings. We fine-tune the model for stress detection and evaluate it on a 40-subject open stress dataset. The evaluation results with a fine-tuned Neuro-GPT are promising with an average accuracy of 74.4% in quantifying "low-stress" and "high-stress". We also conducted experiments to compare the foundation model approach with traditional machine learning methods and highlight several observations for future research in this direction.

Internet of Things (IoT) applications (apps) are challenging to design because of the heterogeneous systems on which they are deployed. IoT devices and apps may collect and analyse sensitive personal data, which is often protected by data privacy laws, some within highly regulated domains such as healthcare. Privacy-by-design (PbD) schemes can be used by developers to consider data privacy at the design stage. However, software developers are not widely adopting these approaches due to difficulties in understanding and interpreting them. There are currently a limited number of tools available for developers to use in this context. We believe that a successful PbD tool should be able to (i) assist developers in addressing privacy requirements in less regulated domains, as well as (ii) help them learn about privacy as they use the tool. The findings of two controlled lab studies are presented, involving 42 developers. We discuss how such a PbD tool can help novice IoT developers comply with privacy laws (e.g., GDPR) and follow privacy guidelines (e.g., privacy patterns). Based on our findings, such tools can help raise awareness of data privacy requirements at design. This increases the likelihood that subsequent designs will be more aware of data privacy requirements.

Industrial cyber-physical systems (ICPS) are widely employed in supervising and controlling critical infrastructures, with manufacturing systems that incorporate industrial robotic arms being a prominent example. The increasing adoption of ubiquitous computing technologies in these systems has led to benefits such as real-time monitoring, reduced maintenance costs, and high interconnectivity. This adoption has also brought cybersecurity vulnerabilities exploited by adversaries disrupting manufacturing processes via manipulating actuator behaviors. Previous incidents in the industrial cyber domain prove that adversaries launch sophisticated attacks rendering network-based anomaly detection mechanisms insufficient as the “physics” involved in the process is overlooked. To address this issue, we propose an IoT-based cyber-physical anomaly detection system that can detect motion-based behavioral changes in an industrial robotic arm. We apply both statistical and state-of-the-art machine learning methods to real-time Inertial Measurement Unit data collected from an edge development board attached to an arm doing a pick-and-place operation. To generate anomalies, we modify the joint velocity of the arm. Our goal is to create an air-gapped secondary protection layer to detect “physical” anomalies without depending on the integrity of network data, thus augmenting overall anomaly detection capability. Our empirical results show that the proposed system, which utilizes 1D convolutional neural networks, can successfully detect motion-based anomalies on a real-world industrial robotic arm. The significance of our work lies in its contribution to developing a comprehensive solution for ICPS security, which goes beyond conventional network-based methods.

Serverless computing offers opportunities for auto-scaling, a pay-for-use cost model, quicker deployment and faster updates to support computing services. Apache OpenWhisk is one such open-source, distributed serverless platform that can be used to execute user functions in a stateless manner. We conduct a performance analysis of OpenWhisk on an edge-cloud continuum, using a function chain of video analysis applications. We consider a combination of Raspberry Pi and cloud nodes to deploy OpenWhisk, modifying a number of parameters, such as maximum memory limit and runtime, to investigate application behaviours. The five main factors considered are: cold and warm activation, memory and input size, CPU architecture, runtime packages used, and concurrent invocations. The results have been evaluated using initialization, and execution time, minimum memory requirement, inference time and accuracy.

Material reuse and recycling plays a key role in reducing carbon emissions in the architecture and construction sector. A “Material Passport” (MP) is a record describing how a material is used throughout its lifetime, from genesis to termination, recording operations carried out on the material. The granularity of information recorded in a MP can vary, however ensuring that this provenance trail remains immutable is a key requirement. The benefits of using a MP, operations carried out on a MP, and recording of transactions within a distributed Blockchain (parachain) is described. A scenario is used to illustrate how the proposed approach can be used in practice.

As Internet of Things (IoT) technologies become more widespread in everyday life , privacy issues are becoming more prominent. The aim of this research is to develop a personal assistant that can answer software engineers’ questions about Privacy by Design (PbD) practices during the design phase of IoT system development. Semantic web technologies are used to model the knowledge underlying PbD measurements, their intersections with privacy patterns, IoT system requirements and the privacy patterns that should be applied across IoT systems. This is achieved through the development of the PARROT ontology, developed through a set of representative IoT use cases relevant for software developers. This was supported by gathering Competency Questions (CQs) through a series of workshops, resulting in 81 curated CQs. These CQs were then recorded as SPARQL queries, and the developed ontology was evaluated using the Common Pitfalls model with the help of the Protégé HermiT Reasoner and the Ontology Pitfall Scanner (OOPS!), as well as evaluation by external experts. The ontology was assessed within a user study that identified that the PARROT ontology can answer up to 58% of privacy-related questions from software engineers.

Due to the data-driven intelligence from the recent deep learning based approaches, the huge amount of data collected from various kinds of sensors from industrial devices have the potential to revolutionize the current technologies used in the industry. To improve the efficiency and quality of machines, the machine manufacturer needs to acquire the history of the machine operation process. However, due to the business secrecy, the factories are not willing to do so. One promising solution to the abovementioned difficulty is the synthetic dataset and an informatic network structure, both through deep generative models such as differentially private generative adversarial networks. Hence, this article initiates the study of the utility difference between the abovementioned two kinds. We carry out an empirical study and find that the classifier generated by private informatic network structure is more accurate than the classifier generated by private synthetic data, with approximately 0.31–7.66%.

Cities are increasingly becoming augmented with sensors through public, private, and academic sector initiatives. Most of the time, these sensors are deployed with a primary purpose (objective) in mind (e.g., deploy sensors to understand noise pollution) by a sensor owner (i.e., the organization that invests in sensing hardware, e.g., a city council). Over the past few years, communities undertaking smart city development projects have understood the importance of making the sensor data available to a wider community—beyond their primary usage. Different business models have been proposed to achieve this, including creating data marketplaces. The vision is to encourage new startups and small and medium-scale businesses to create novel products and services using sensor data to generate additional economic value. Currently, data are sold as pre-defined independent datasets (e.g., noise level and parking status data may be sold separately). This approach creates several challenges, such as (i) difficulties in pricing, which leads to higher prices (per dataset); (ii) higher network communication and bandwidth requirements; and (iii) information overload for data consumers (i.e., those who purchase data). We investigate the benefit of semantic representation and its reasoning capabilities toward creating a business model that offers data on demand within smart city Internet of Things data marketplaces. The objective is to help data consumers (i.e., small and medium enterprises) acquire the most relevant data they need. We demonstrate the utility of our approach by integrating it into a real-world IoT data marketplace (developed by the synchronicity-iot.eu project). We discuss design decisions and their consequences (i.e., tradeoffs) on the choice and selection of datasets. Subsequently, we present a series of data modeling principles and recommendations for implementing IoT data marketplaces.

The balance between protecting user privacy while providing cost-effective devices that are functional and usable is a key challenge in the burgeoning Internet of Things (IoT). In traditional desktop and mobile contexts, the primary user interface is a screen; however, in IoT devices, screens are rare or very small, invalidating many existing approaches to protecting user privacy. Privacy visualizations are a common approach for assisting users in understanding the privacy implications of web and mobile services. To gain a thorough understanding of IoT privacy, we examine existing web, mobile, and IoT visualization approaches. Following that, we define five major privacy factors in the IoT context: type, usage, storage, retention period, and access. We then describe notification methods used in various contexts as reported in the literature. We aim to highlight key approaches that developers and researchers can use for creating effective IoT privacy notices that improve user privacy management (awareness and control). Using a toolkit, a use case scenario, and two examples from the literature, we demonstrate how privacy visualization approaches can be supported in practice.

Our aim is to create a personal assistant, a chatbot, that can answer queries from software developers regarding Privacy by Design (PbD) methods and applications throughout the design phase of IoT system development. We used semantic web technologies to model the PARROT Ontology that includes knowledge underlying PbD measurements, their intersections with privacy patterns, IoT system needs, and the privacy patterns that should be applied across IoT systems. To determine the PARROT ontology's requirements, a collection of real-world IoT use cases were aided by a series of workshops to gather Competency Questions (CQs) from researchers and software engineers, resulting in 81 selected CQs. In a user study, the PARROT ontology was able to answer up to 58% of software developers' privacy-related issues. The technical report \citeorca149337 contains further analysis and results from data collecting and intermediate synthesis steps.

The mutually beneficial blend of artificial intelligence with internet of things has been enabling many industries to develop smart information processing solutions. The implementation of technology enhanced industrial intelligence systems is challenging with the environmental conditions, resource constraints and safety concerns. With the era of smart homes and cities, domains like automated license plate recognition (ALPR) are exploring automate tasks such as traffic management and fraud detection. This paper proposes an optimized decision support solution for ALPR that works purely on edge devices at night-time. Although ALPR is a frequently addressed research problem in the domain of intelligent systems, still they are generally computationally intensive and unable to run on edge devices with limited resources. Therefore, as a novel approach, we consider the complex aspects related to deploying lightweight yet efficient and fast ALPR models on embedded devices. The usability of the proposed models is assessed in real-world with a proof-of-concept hardware design and achieved competitive results to the state-of-the-art ALPR solutions that run on server-grade hardware with intensive resources.

The Internet of Things (IoT) aims to connect everyday physical objects to the internet. These objects will produce a significant amount of data. The traditional cloud computing architecture aims to process data in the cloud. As a result, a significant amount of data needs to be communicated to the cloud. This creates a number of challenges, such as high communication latency between the devices and the cloud, increased energy consumption of devices during frequent data upload to the cloud, high bandwidth consumption, while making the network busy by sending the data continuously, and less privacy because of less control on the transmitted data to the server. Fog computing has been proposed to counter these weaknesses. Fog computing aims to process data at the edge and substantially eliminate the necessity of sending data to the cloud. However, combining the Service Oriented Architecture (SOA) with the fog computing architecture is still an open challenge. In this paper, we propose to decompose services to createlinked-microservices(LMS).Linked-microservicesare services that run on multiple nodes but closely linked to their linked-partners.Linked-microservicesallow distributing the computation across different computing nodes in the IoT architecture. Using four different types of architectures namely cloud, fog, hybrid, and fog+cloud, we explore and demonstrate the effectiveness of service decomposition by applying four experiments to three different type of datasets. Evaluation of the four architectures shows that decomposing services into nodes reduce the data consumption over the network by 10 - 70 percent. Overall, these results indicate that the importance of decomposing services in the context of fog computing for enhancing the quality of service.

Today we are relying on Internet technologies for numerous services, for example, personal communication, online businesses, recruitment, and entertainment. Over these networks, people usually create content, a skillful worker profile, and provide services that are normally watched and used by other users, thus developing a social network among people termed as the Internet of People. Malicious users could also utilize such platforms for spreading unwanted content that could bring catastrophic consequences to a social network provider and the society, if not identified on time. The use of trust management over these networks plays a vital role in the success of these services. Crowd-sensing people or network users for their views about certain content or content creators could be a potential solution to assess the trustworthiness of content creators and their content. However, the human involvement in crowd-sensing would have challenges of privacy preservation and preventing intentional assignment of the fake high score given to certain user/content. To address these challenges, in this article, we propose a novel trust model that evaluates the aggregate trustworthiness of the content creator and the content without compromising the privacy of the participating people in a crowdsource group. The proposed system has inherent properties of privacy protection of participants, performs operations in the decentralized setup, and considers the trust weights of participants in a private and secure way. The system ensures privacy of participants under the malicious and honest-but-curious adversarial models. We evaluated the performance of the system by developing a prototype and applying it to different real data from different online social networks.

introduction Share on Introduction to the Special Section on Human-centered Security, Privacy, and Trust in the Internet of Things Editors: Mahmoud Barhamgi View Profile , Michael N. Huhns View Profile , Charith Perera View Profile , Pinar Yolum View Profile Authors Info & Claims ACM Transactions on Internet TechnologyVolume 21Issue 1February 2021 Article No.: 16pp 1–3https://doi.org/10.1145/3445790Online:20 January 2021Publication History 1citation157DownloadsMetricsTotal Citations1Total Downloads157Last 12 Months98Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

In today’s highly connected cyber-physical environments, users are becoming more and more concerned about their privacy and ask for more involvement in the control of their data. However, achieving effective involvement of users requires improving their privacy decision-making. This can be achieved by: (i) raising their awareness regarding the direct and indirect privacy risks they accept to take when sharing data with consumers; (ii) helping them in optimizing their privacy protection decisions to meet their privacy requirements while maximizing data utility. In this article, we address the second goal by proposing a user-centric multi-objective approach for context-aware privacy management in connected environments, denoted δ- Risk . Our approach features a new privacy risk quantification model to dynamically calculate and select the best protection strategies for the user based on her preferences and contexts. Computed strategies are optimal in that they seek to closely satisfy user requirements and preferences while maximizing data utility and minimizing the cost of protection. We implemented our proposed approach and evaluated its performance and effectiveness in various scenarios. The results show that δ- Risk delivers scalability and low-complexity in time and space. Besides, it handles privacy reasoning in real-time, making it able to support the user in various contexts, including ephemeral ones. It also provides the user with at least one best strategy per context.

Privacy by Design (PbD) is the most common approach followed by software developers who aim to reduce risks within their application designs, yet it remains commonplace for developers to retain little conceptual understanding of what is meant by privacy. A vision is to develop an intelligent privacy assistant to whom developers can easily ask questions to learn how to incorporate different privacy-preserving ideas into their IoT application designs. This article lays the foundations toward developing such a privacy assistant by synthesising existing PbD knowledge to elicit requirements. It is believed that such a privacy assistant should not just prescribe a list of privacy-preserving ideas that developers should incorporate into their design. Instead, it should explain how each prescribed idea helps to protect privacy in a given application design context—this approach is defined as “Explainable Privacy.” A total of 74 privacy patterns were analysed and reviewed using ten different PbD schemes to understand how each privacy pattern is built and how each helps to ensure privacy. Due to page limitations, we have presented a detailed analysis in Reference [3]. In addition, different real-world Internet of Things (IoT) use-cases, including a healthcare application, were used to demonstrate how each privacy pattern could be applied to a given application design. By doing so, several knowledge engineering requirements were identified that need to be considered when developing a privacy assistant. It was also found that, when compared to other IoT application domains, privacy patterns can significantly benefit healthcare applications. In conclusion, this article identifies the research challenges that must be addressed if one wishes to construct an intelligent privacy assistant that can truly augment software developers’ capabilities at the design phase.

The Internet of Things (IoT) or the cyber-physical system (CPS) is the network of connected devices, things, and people that collect and exchange information using the emerging telecommunication networks (4G, 5G IP-based LTE). These emerging telecommunication networks can also be used to transfer critical information between the source and destination, informing the control system about the outage in the electrical grid, or providing information about the emergency at the national express highway. This sensitive information requires authorization and authentication of source and destination involved in the communication. To protect the network from unauthorized access and to provide authentication, the telecommunication operators have to adopt the mechanism for seamless verification and authorization of parties involved in the communication. Currently, the next-generation telecommunication networks use a digest-based authentication mechanism, where the call-processing engine of the telecommunication operator initiates the challenge to the request-initiating client or caller, which is being solved by the client to prove his credentials. However, the digest-based authentication mechanisms are vulnerable to many forms of known attacks, e.g., the man-in-the-middle (MITM) attack and the password guessing attack. Furthermore, the digest-based systems require extensive processing overheads. Several public-key infrastructure (PKI)-based and identity-based schemes have been proposed for the authentication and key agreements. However, these schemes generally require a smart card to hold long-term private keys and authentication credentials. In this article, we propose a novel self-enforcing authentication protocol for the session-initiation-protocol-based next-generation network, based on a low-entropy shared password without relying on any PKI or the trusted third party system. The proposed system shows effective resistance against various attacks, e.g., MITM, replay attack, password guessing attack, etc. We analyze the security properties of the proposed scheme in comparison to the state of the art.

The design and development process for internet of things (IoT) applications is more complicated than that for desktop, mobile, or web applications. First, IoT applications require both software and hardware to work together across many different types of nodes with different capabilities under different conditions. Second, IoT application development involves different types of software engineers such as desktop, web, embedded, and mobile to work together. Furthermore, non-software engineering personnel such as business analysts are also involved in the design process. In addition to the complexity of having multiple software engineering specialists cooperating to merge different hardware and software components together, the development process requires different software and hardware stacks to be integrated together (e.g., different stacks from different companies such as Microsoft Azure and IBM Bluemix). Due to the above complexities, non-functional requirements (such as security and privacy, which are highly important in the context of the IoT) tend to be ignored or treated as though they are less important in the IoT application development process. This article reviews techniques, methods, and tools to support security and privacy requirements in existing non-IoT application designs, enabling their use and integration into IoT applications. This article primarily focuses on design notations, models, and languages that facilitate capturing non-functional requirements (i.e., security and privacy). Our goal is not only to analyse, compare, and consolidate the empirical research but also to appreciate their findings and discuss their applicability for the IoT.

In modern information systems different information features, about the same individual, are often collected and managed by autonomous data collection services that may have different privacy policies. Answering many end-users' legitimate queries requires the integration of data from multiple such services. However, data integration is often hindered by the lack of a trusted entity, often called a mediator, with which the services can share their data and delegate the enforcement of their privacy policies. In this article, we propose a flexible privacy-preserving data integration approach for answering data integration queries without the need for a trusted mediator. In our approach, services are allowed to enforce their privacy policies locally. The mediator is considered to be untrusted, and only has access to encrypted information to allow it to link data subjects across the different services. Services, by virtue of a new privacy requirement, dubbed k-Protection, limiting privacy leaks, cannot infer information about the data held by each other. End-users, in turn, have access to privacy-sanitized data only. We evaluated our approach using an example and a real dataset from the healthcare application domain. The results are promising from both the privacy preservation and the performance perspectives.

With the ubiquity of wireless network and the intelligentization of machines, Internet of Things (IoT) has come to people's horizon. Device-to-device (D2D), as one advanced technique to achieve the vision of IoT, supports a high speed peer-to-peer transmission without fixed infrastructure forwarding which can enable fast content distribution in local area. In this paper, we address the content distribution problem by multihop D2D communication with decentralized content providers locating in the networks. We consider a cross-layer multidimension optimization involving frequency, space, and time, to minimize the network average delay. Considering the multicast feature, we first formulate the problem as a coalitional game based on the payoffs of content requesters, and then, propose a time-varying coalition formation-based algorithm to spread the popular content within the shortest possible time. Simulation results show that the proposed approach can achieve a fast content distribution across the whole area, and the performance on network average delay is much better than other heuristic approaches.

Every day more and more objects are connected to the Internet to sense or actuate in some environment, composing the Internet of Things. IoT platforms will play a key role, as they will be responsible for managing low-level devices and data acquisition processes, and also support the development of new applications. One of the main challenges in IoT platforms will be the search and discovery of resources in large-scale and heterogeneous environments for reuse by other applications to support their specific requirements. In this paper, we propose an elimination-selection algorithm for search and discovery of resources in IoT environments. Our case study considers a real agricultural problem to be solved by the ViSIoT tool. The results show that our approach improves the quality of the proposed solution adding a small time overhead when compared to the TOPSIS algorithm used by ViSIoT.

Over the last few years, a large number of Internet of Things (IoT) solutions have come to the IoT marketplace. Typically, each of these IoT solutions are designed to perform a single or minimal number of tasks (primary usage). We believe a significant amount of knowledge and insights are hidden in these data silos that can be used to improve our lives; such data include our behaviors, habits, preferences, life patterns, and resource consumption. To discover such knowledge, we need to acquire and analyze this data together in a large scale. To discover useful information and deriving conclusions toward supporting efficient and effective decision making, industrial IoT platform needs to support variety of different data analytics processes such as inspecting, cleaning, transforming, and modeling data, especially in big data context. IoT middleware platforms have been developed in both academic and industrial settings in order to facilitate IoT data management tasks including data analytics. However, engineering these general-purpose industrial-grade big data analytics platforms need to address many challenges. We have accepted six manuscripts out of 24 submissions for this special section (25% acceptance rate) after the strict peerreview processes. Each manuscript has been blindly reviewed by at least three external reviewers before the decisions were made. The papers are briefly summarized.

Smart grid (SG) is an integration of traditional power grid with advanced information and communication infrastructure for bidirectional energy flow between grid and end users. A huge amount of data is being generated by various smart devices deployed in SG systems. Such a massive data generation from various smart devices in SG systems may lead to various challenges for the networking infrastructure deployed between users and the grid. Hence, an efficient data transmission technique is required for providing desired QoS to the end users in this environment. Generally, the data generated by smart devices in SG has high dimensions in the form of multiple heterogeneous attributes, values of which are changed with time. The high dimensions of data may affect the performance of most of the designed solutions in this environment. Most of the existing schemes reported in the literature have complex operations for the data dimensionality reduction problem which may deteriorate the performance of any implemented solution for this problem. To address these challenges, in this paper, a tensor-based big data management scheme is proposed for dimensionality reduction problem of big data generated from various smart devices. In the proposed scheme, first the Frobenius norm is applied on high-order-tensors (used for data representation) to minimize the reconstruction error of the reduced tensors. Then, an empirical probability-based control algorithm is designed to estimate an optimal path to forward the reduced data using software-defined networks for minimization of the network load and effective bandwidth utilization. The proposed scheme minimizes the transmission delay incurred during the movement of the dimensionally reduced data between different nodes. The efficacy of the proposed scheme has been evaluated using extensive simulations carried out on the data traces using `R' programming and Matlab. The big data traces considered for evaluation consist of more than two million entries (2,075,259) collected at one minute sampling rate having hetrogenous features such as-voltage, energy, frequency, electric signals, etc. Moreover, a comparative study for different data traces and a real SG testbed is also presented to prove the efficacy of the proposed scheme. The results obtained depict the effectiveness of the proposed scheme with respect to the parameters such asnetwork delay, accuracy, and throughput.

The long-heralded Internet of Things (IoT) is finally becoming a reality. From factories and the ubiquitous Internet-connected fridge, we now see heating control systems, cars, dishwashers, and all manner of common-place devices being connected. While this has certainly realized new capabilities, such as the ability to control one’s domestic heating remotely, the benefits are perhaps more mixed: every device that we can remotely control is a device that someone else can remotely hack. And that is just the devices—the literal things; in tandem we also see increasing intrusion of Internet-connectivity into services, practices and everyday infrastructures such as transport and retail. Coupled with the increasingly invasive deployment of these devices into everyday lives, the result is a substantial increase in threats to privacy arising from the IoT.

Summary A large number of cloud middleware platforms and tools are deployed to support a variety of internet‐of‐things (IoT) data analytics tasks. It is a common practice that such cloud platforms are only used by its owners to achieve their primary and predefined objectives, where raw and processed data are only consumed by them. However, allowing third parties to access processed data to achieve their own objectives significantly increases integration and cooperation and can also lead to innovative use of the data. Multi‐cloud, privacy‐aware environments facilitate such data access, allowing different parties to share processed data to reduce computation resource consumption collectively. However, there are interoperability issues in such environments that involve heterogeneous data and analytics‐as‐a‐service providers. There is a lack of both architectural blueprints that can support such diverse, multi‐cloud environments and corresponding empirical studies that show feasibility of such architectures. In this paper, we have outlined an innovative hierarchical data‐processing architecture that utilises semantics at all the levels of IoT stack in multi‐cloud environments. We demonstrate the feasibility of such architecture by building a system based on this architecture using OpenIoT as a middleware, and Google Cloud and Microsoft Azure as cloud environments. The evaluation shows that the system is scalable and has no significant limitations or overheads. Copyright © 2016 John Wiley & Sons, Ltd.

Summary The growth of real‐world objects with embedded and globally networked sensors allows to consolidate the Internet of things paradigm and increase the number of applications in the domains of ubiquitous and context‐aware computing. The merging between cloud computing and Internet of things named cloud of things will be the key to handle thousands of sensors and their data. One of the main challenges in the cloud of things is context‐aware sensor search and selection. Typically, sensors require to be searched using two or more conflicting context properties. Most of the existing work uses some kind of multi‐criteria decision analysis to perform the sensor search and selection, but does not show any concern for the quality of the selection presented by these methods. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi‐objective decision methods and their quality of selection comparing them with thePareto‐optimality solutions. The gathered results allow to analyse and compare these algorithms regarding their behaviour, the number of optimal solutions and redundancy. Copyright © 2016 John Wiley & Sons, Ltd.

The application of mobile computing is currently altering patterns of our behavior to a greater degree than perhaps any other invention. In combination with the introduction of power-efficient wireless communication technologies, such as Bluetooth Low Energy (BLE), designers are today increasingly empowered to shape the way we interact with our physical surroundings and thus build entirely new experiences. However, our evaluations of BLE and its abilities to facilitate mobile location-based experiences in public environments revealed a number of potential problems. Most notably, the position and orientation of the user in combination with various environmental factors, such as crowds of people traversing the space, were found to cause major fluctuations of the received BLE signal strength. These issues are rendering a seamless functioning of any location-based application practically impossible. Instead of achieving seamlessness by eliminating these technical issues, we thus choose to advocate the use of a seamful approach, that is, to reveal and exploit these problems and turn them into a part of the actual experience. In order to demonstrate the viability of this approach, we designed, implemented, and evaluated the Ghost Detector —an educational location-based museum game for children. By presenting a qualitative evaluation of this game and by motivating our design decisions, this article provides insight into some of the challenges and possible solutions connected to the process of developing location-based BLE-enabled experiences for public cultural spaces.

The Internet of Things (IoT) envisions billions of sensors deployed around us and connected to the Internet, where the mobile crowd sensing technologies are widely used to collect data in different contexts of the IoT paradigm. Due to the popularity of Big Data technologies, processing and storing large volumes of data have become easier than ever. However, large-scale data management tasks still require significant amounts of resources that can be expensive regardless of whether they are purchased or rented (e.g., pay-as-you-go infrastructure). Further, not everyone is interested in such large-scale data collection and analysis. More importantly, not everyone has the financial and computational resources to deal with such large volumes of data. Therefore, a timely need exists for a cloud-integrated mobile crowd sensing platform that is capable of capturing sensors data, on-demand, based on conditions enforced by the data consumers. In this paper, we propose a context-aware, specifically, location and activity-aware mobile sensing platform called context-aware mobile sensor data engine (C-MOSDEN) for the IoT domain. We evaluated the proposed platform using three real-world scenarios that highlight the importance of selective sensing. The computational effectiveness and efficiency of the proposed platform are investigated and are used to highlight the advantages of context-aware selective sensing.

Mobile devices are rapidly becoming the primary computing device in people’s lives. Application delivery platforms like Google Play, Apple App Store have transformed mobile phones into intelligent computing devices by the means of applications that can be downloaded and installed instantly. Many of

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a substantial acceleration of the growth rate in the future. It is also evident that the increasing number of IoT middleware solutions are developed in both research and commercial environments. However, sensor search and selection remain a critical requirement and a challenge. In this paper, we present CASSARAM, a context-aware sensor search, selection, and ranking model for Internet of Things to address the research challenges of selecting sensors when large numbers of sensors with overlapping and sometimes redundant functionality are available. CASSARAM proposes the search and selection of sensors based on user priorities. CASSARAM considers a broad range of characteristics of sensors for search such as reliability, accuracy, battery life just to name a few. Our approach utilises both semantic querying and quantitative reasoning techniques. User priority based weighted Euclidean distance comparison in multidimensional space technique is used to index and rank sensors. Our objectives are to highlight the importance of sensor search in IoT paradigm, identify important characteristics of both sensors and data acquisition processes which help to select sensors, understand how semantic and statistical reasoning can be combined together to address this problem in an efficient manner. We developed a tool called CASSARA to evaluate the proposed model in terms of resource consumption and response time.

Mobile phones play increasingly bigger role in our everyday lives. Today, most smart phones comprise a wide variety of sensors which can sense the physical environment. The Internet of Things vision encompasses participatory sensing which is enabled using mobile phones based sensing and reasoning. In this research, we propose and demonstrate our DAM4GSN architecture to capture sensor data using sensors built into the mobile phones. Specifically, we combine an open source sensor data stream processing engine called ‘Global Sensor Network (GSN)’ with the Android platform to capture sensor data. To achieve this goal, we proposed and developed a prototype application that can be installed on Android devices as well as a AndroidWrapper as a GSN middleware component. The process and the difficulty of manually connecting sensor devices to sensor data processing middleware systems are examined. We evaluated the performance of the system based on power consumption of the mobile client.