The Internet of Things (IoT) in the world is drastically increasing to make environments more efficient, user-friendly, and automated. However, cyberattacks on these IoT devices continue to be a significant t hreat. I n t his a rticle, w e p ropose a novel Artificial Intelligence (AI)-based middleware and model for middleware to detect attacks in versatile Smart Environments. To assess the applicability of the proposed method, we designed four-step process for data-driven multi-agent malware and attack detection. The first s tep c orresponds w ith t he a ggregation of multi-level network traffic d ata f rom s everal I oT d evices s uch as Arduino, Rasberry Pi, NVIDIA Jetson devices, etc. In the second phase, different AI and Deep Learning models are applied for multi-level malware and attack classifications, a nd t he efficiency of the off-chip inferred AI model is evaluated with a motive to reduce the overhead and latency of the IoT components. In the third step, we deploy the different versions of the inferred model on our heterogeneous local smart environments. Finally, as the fourth step, the performance and concurrency testing in terms of electrical power, network bandwidth, and memory usage by the model is measured to check how efficient the Artificial Intelligence method is towards IoT cybersecurity for smart environments. The experimental results on the heterogeneous IoT malware and attack datasets inspected in this study suggest AI can be used as an effective tool to prevent the smart environment from cybersecurity threats.

The Internet of Things (IoT) paradigm has applications in many domains and is growing these days progressively. The applications are e-business, e-healthcare, and e-transportation. Recently, the container microservices-based Mobile Cloud Computing (MCC) has gained popularity, a lightweight framework compared to the monolithic virtual machine-based system. MCC combines fog nodes or cloud nodes with a base station to run the applications. However, storing the sensitive data of IoT applications on the untrusted nodes and failure of services are critical challenges in the existing architecture. This study proposes a novel microservices-based by combining fog and cloud services with efficient schemes. The first scheme is the Latency Aware Task Assignment Algorithm, which determines the optimal assignment of tasks to minimize the makespan of all applications. The second scheme is Fully Homomorphism Encryption, which ensures data security before an offload to any external assistance for execution. The final one is the Failure Aware, which handles any transient failure during application execution in the architecture. The experimental results show that the recommended architecture improved resource utilization, and the proposed schemes satisfied the security demand while reducing the makespan of applications.

Traditionally, survey data and travel data are considered and analyzed independently. By being able to combine survey data with the respective trip data, this paper analyzes patterns between quantitative mobility data and qualitative survey responses. Firstly, we apply spatial-temporal clustering on the mobility data to understand travel patterns. Secondly, we utilize association rule mining to understand the differences between the clusters. Lastly, we apply association rule mining on the combined mobility and survey data set to understand the perception of Covid-19 related measurements in public transportation. With the created association rules, public transportation authorities can comprehend how different measurements affect the awareness of their services.

Smart Environments contribute towards better, user-friendly and automated environments; however, often lacking means of implementation of necessary cybersecurity measures capable of handling adversarial actions. In this paper we will introduce the anticipated cyber threats and corresponding ambition of the ENViSEC project that is aimed at enhancing the Smart Environments cybersecurity by introducing intelligent multi-agent data handling and cyber threats sharing. The potential of the project is to introduce situational awareness and data streams from Internet of Things ecosystem to offer a resilient response to cyber-attacks. This will ensure human-oriented awareness and early detection of cybercrimes in the era of Big Data. The new approach will include multi-level data aggregation and off-chip Machine Learning model training to reduce the overhead and latency of the IoT components yet guarantee the necessary level of hardening cybersecurity in a cross-sector context.

Millions of applications are being developed around the world with a promise of increasing our productivity, teaching us new skills, improving our health and providing us with entertainment. In order to cater to the users needs, ever more intricate and advanced applications are being developed and with more sophisticated solutions, an increase in resource expenditure is likely to occur. Smartphones have limited resources in terms of both computational performance and battery capacity and most developers today tries to optimise their applications in order to provide the best user experience with as low a resource cost as possible. In this paper, large amounts of performance data has been gathered from different Android applications and games on different devices in order to establish a baseline which in turn can be used for training of machine learning algorithms for usage in automatic testing and real-time performance assessment solutions.

In this paper we demonstrate the feasibility of IoT deployment for noise level measurement to time-limited and high-intense, high-volume data, events. Through an iterative process, a prototype solution were designed and implemented in a real-time, privacy-compliant IoT sensor system under tight constraints concerning budget and development time. Our sensor system enables festival management to easily track, document and further, by applying real time big data analytics to the harvested information, have fact-full insights generated for decision making in terms of resolving noise disturbances. The whole approach was demonstrated by the use of lightweight Internet of Things architecture demonstrating how web technologies can be used throughout the technology stack in and IoT big data analytics case.