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The rapid development of IoT sensors and data provided by Social Networks has necessitated the fast development of recommender systems as they can be used as a tool to filter items that are more likely to be preferred by users. A major goal of recommender systems is to provide users with personalized recommendations after analyzing their preferences. IoT smart devices and Social Networks have opened windows of opportunities for user preferences to be dynamically recognized. Although analyzing user preferences helps to provide more personalized recommendations, considering location and orientation information as user contextual information results in more relevant recommendations being provided. Location information has been used by Social Networks, especially Location-Based Social Networks, in order to provide recommendations based on current user location. However, the importance of the user orientation context has been overlooked by almost all of the research done in this area. Developing a location-based orientation-aware recommender system can perfectly bridge this gap. For this study, a location-based orientation-aware recommender system is proposed as an innovative type of recommender system. The proposed recommender system is able to not only apply contemporary user contextual information to the recommender algorithm, but also makes progress towards preparing more personalized recommendations by taking user orientation context into account. For this study, user preferences are dynamically measured by IoT smart devices such as smartphones, Google Home, and smartwatches. Information provided by virtual communities extracted from Social Networks helps the recommender system in situations in which user preferences are not extracted from their IoT devices. In addition to user preferences, their smartphone pointing direction has also been applied as their orientation context for the recommender algorithm in outdoor environments. To evaluate the impact of the user pointing direction in our proposed methodology, an event recommender system based on the real data was implemented and examined in the city of Tehran in Iran. Because of the challenging nature of social events, a simulated experiment is also presented for the City of Calgary. Also, the system results are compared with the results of Collaborative Filtering and Content-based recommender algorithms to demonstrate the power of the recommendation engine. The evaluation indexes prove that our proposed recommender system outperforms its counterparts by providing more accurate and personalized recommendations.

One of the most important challenges of volunteered geographic information (VGI) is the quality assessment. Existing methods of VGI quality assessment, either assess the quality by comparing a reference map with the VGI map or deriving the quality from the metadata. The first approach does not work for a real-time scenario and the latter delivers approximate values of the quality. Internet of Things (IoT) networks provide real-time observations for environment monitoring. Moreover, they publish more precise information than VGI. This paper introduces a method to assess the quality of VGI in real-time using IoT observations. The proposed method filters sensor observation outliers in the first step. Then it matches sensors and volunteers’ relationships in terms of location, time, and measurement type similarity using a hypergraph model. Then the quality of matched data is assessed by calculating positional and attribute accuracy. To evaluate the method, VGI data of the water level and quality in Tarashk–Bakhtegan–Maharlou water basin is studied. A VGI quality map of the data is assessed by a referenced authoritative map. The output of this step is a VGI quality map, which was used as a reference to check the proposed method quality. Then this reference VGI quality map and the proposed method VGI quality map are compared to assess positional and attribute accuracy. Results demonstrated that 76% of the method results have less than 20 m positional error (i.e., difference with the reference VGI quality map). Additionally, more than 92% of the proposed method VGI data have higher than 90% attribute accuracy in terms of similarity with the reference VGI quality map. These findings support the notion that the proposed method can be used to assess VGI quality in real-time.

With the wide availability of low-cost proximity sensors, a large body of research focuses on digital person-to-person contact tracing applications that use proximity sensors. In most contact tracing applications, the impact of SARS-CoV-2 spread through touching contaminated surfaces in enclosed places is overlooked. This study is focused on tracing human contact within indoor places using the open OGC IndoorGML standard. This paper proposes a graph-based data model that considers the semantics of indoor locations, time, and users&rsquo, contexts in a hierarchical structure. The functionality of the proposed data model is evaluated for a COVID-19 contact tracing application with scalable system architecture. Indoor trajectory preprocessing is enabled by spatial topology to detect and remove semantically invalid real-world trajectory points. Results show that 91.18% percent of semantically invalid indoor trajectory data points are filtered out. Moreover, indoor trajectory data analysis is innovatively empowered by semantic user contexts (e.g., disinfecting activities) extracted from user profiles. In an enhanced contact tracing scenario, considering the disinfecting activities and sequential order of visiting common places outperformed contact tracing results by filtering out unnecessary potential contacts by 44.98 percent. However, the average execution time of person-to-place contact tracing is increased by 58.3%.

One of the constraints in Wireless Sensor Network (WSN) is the limited power of distributed interconnected sensor nodes over the space. There are several proposed architectures for handling power consumption in a WSN. Traditional clustering and routing methods improve the Cluster Head (CH) selection of WSN internodes for balancing energy in the network. In such clustering methods, every message is transmitted from a source node to its CH, from the CH to other CHs, and finally to the BS (Base Station). Therefore, a high amount of energy should be consumed for routing when the source nodes are far from CHs or the BS. This paper proposes an integrated LBSN (Location-Based Social Network) and WSN routing and CH selection method for reducing routes from source nodes to BSs to reduce the energy consumption of WSN. In the integrated network, LBSN nodes can be added to the WSN as BS or WSN sensor nodes. In the first situation, the integrated network is called IPAWL I and the latter builds the IPAWL II network.To evaluate the method, we considered a scenario of smoke detection and reporting on the campus of the University of Calgary and generated a simulation dataset for the LBSN and WSN. Then, results of implementing IPAWL I, IPAWL II, and a stand-alone WSN on the dataset were compared in terms of network energy consumption and message transmission latency. Results demonstrated a 42% reduction in energy consumption in IPAWL I and 33% in IPAWL II. Moreover, a significant reduction of time latency was observed in bigger size networks.

Particulate matter with a diameter less than 2.5 micrometers (PM2.5) can be considered as the most dangerous air pollutant that affects human health. In addition, technological advances, such as those involving the Internet of Things (IoT) for monitoring air quality, have made it possible to monitor air quality for a lower cost. However, missing values and noisy data make nonlinear data provided by air quality IoT sensors less reliable and more complicated than data provided by air quality monitoring stations. In this study, we propose a mixed edge-based and cloud-based framework with the final goal of PM2.5 value prediction. In order to validate the proposed approach, we evaluate the quality of predictions using both original and preprocessed data on a real-world dataset from air quality sensors distributed in Calgary, Canada. Obtained results show an average improvement of 40.18% of the prediction accuracy on Mean Absolute Percentage Error by using the proposed preprocessing technique.

Context-aware systems are intelligent systems that recommend users with adaptive service choices which are appropriate to their profile preferences and contextual situations. However, the key challenges in these systems are extracting dynamic context information and updating user's profile information. Recently by the popularity of IoT devices as well as the availability of the wireless network, a massive volume of data is available to gain useful insights into user profile which contains dynamic contextual information. IoT sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data, we need to understand them and use them in a context-aware computing platform. In this article, an adaptive context-aware service composition system is proposed using an IoT based social network dataset to find users' preferences, improve quality of experience, and boosts the personalization level of services. This paper proposes an 10T-based context-aware service composition to seamlessly find adaptive services offered by a service provider at anytime and anywhere. Firstly, the proposed system considers the dynamic social profile of the user and clusters their preferences while other systems take only static user interests into account. Secondly, this system increases the level of personalization by extracting context information from a user's IoT devices. Finally, the adaptive service composition provides users with various context-aware recommendation including movies, music, venues, radio stations, and events simultaneously. To evaluate the efficiency of the proposed system, we target some of the smart objects such as smart watches/bands, smartphones/tablets, Google Home, and personal computers for detecting several contexts (e.g location, time, user activity, interest and e.t.c.). Experimental evaluations show that the proposed algorithm can improve accuracy and personalization of filtered items with compare to other widely-applied conventional filtering algorithms.

To safely protect workplaces and the workforce during and after the COVID-19 pandemic, a scalable integrated sensing solution is required in order to offer real-time situational awareness and early warnings for decision-makers. However, an information-based solution for industry reopening is ineffective when the necessary operational information is locked up in disparate real-time data silos. There is a lot of ongoing effort to combat the COVID-19 pandemic using different combinations of low-cost, location-based contact tracing, and sensing technologies. These ad hoc Internet of Things (IoT) solutions for COVID-19 were developed using different data models and protocols without an interoperable way to interconnect these heterogeneous systems and exchange data on people and place interactions. This research aims to design and develop an interoperable Internet of COVID-19 Things (IoCT) architecture that is able to exchange, aggregate, and reuse disparate IoT sensor data sources in order for informed decisions to be made after understanding the real-time risks in workplaces based on person-to-place interactions. The IoCT architecture is based on the Sensor Web paradigm that connects various Things, Sensors, and Datastreams with an indoor geospatial data model. This paper presents a study of what, to the best of our knowledge, is the first real-world integrated implementation of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) and IndoorGML standards to calculate the risk of COVID-19 online using a workplace reopening case study. The proposed IoCT offers a new open standard-based information model, architecture, methodologies, and software tools that enable the interoperability of disparate COVID-19 monitoring systems with finer spatial-temporal granularity. A workplace cleaning use case was developed in order to demonstrate the capabilities of this proposed IoCT architecture. The implemented IoCT architecture included proximity-based contact tracing, people density sensors, a COVID-19 risky behavior monitoring system, and the contextual building geospatial data.