Mobile Internet-of-Things (IoT) applications increasingly demand low-latency service delivery and efficient content dissemination, yet conventional cloud-centric solutions often suffer from excessive backhaul delay.We present an energy-aware floating architecture that enables peer-assisted edge computing and cooperative content caching across mobile IoT devices. The framework comprises three key components: (i) Floating Service, where selected user nodes temporarily act as local servers and adapt in real time to node mobility to sustain seamless service provisioning; (ii) Cooperative Caching, which supports dynamic, proximity-based content exchange among neighboring devices; and (iii) a mobility-aware service-migration predictor that forecasts user movement to guide timely service handover and cache preloading. A radio duty-cycle energy model records transmit, receive, and idle activity to estimate device-side power expenditure. Extensive simulations show that the proposed system reduces end-to-end latency by up to 87.5% for more than 90% of service requests, while the cooperative caching component lowers retrieval delay by up to 58.1% for 28% of communications. Crucially, the floating mechanism increases the average device radio duty cycle by only 1–3 percentage points, corresponding to an incremental power cost of just 3.6–24.6 mW per node. These results underscore the practicality of energy-aware, peer-assisted edge collaboration for next-generation mobile IoT deployments.

Transformer-based architectures have introduced end-to-end solutions for Multiple Object Tracking (MOT), seamlessly integrating object detection and association. However, their high computational demands—such as the need for feature map fusion across multiple frames—pose significant challenges to real-time deployment, limiting their practicality. In this paper, we present WT-MOT (Weight Table-based Multiple Object Tracking), a novel framework that addresses these limitations by leveraging the underutilized potential of attention weight tables for efficient object similarity evaluation. WT-MOT employs self- and cross-attention mechanisms to assess object similarity and directly assign identifications, integrating spatial, appearance, and temporal dimensions. By introducing the “frame embedding” concept, WT-MOT enhances the ability to distinguish objects across frames without relying on motion models or post-processing steps. Experimental results on the MOT17 and MOT20 benchmarks demonstrate the effectiveness of WT-MOT, achieving MOTA scores of 76.9% and 73.2%, respectively, setting new performance standards for Transformer-based MOT solutions. These findings highlight WT-MOT as a computationally efficient and robust tool for real-time MOT applications, paving the way for broader adoption of Transformer-based tracking methods in practical environments.

The rapidly evolving realm of Extended Reality (XR) demands high bandwidth and low-latency communication to support immersive experiences such as high-resolution 360-degree videos and real-time interactions in virtual reality gaming. In this study, “resources” are defined as digital assets essential for XR applications, divided into “static resources” (immutable media files such as textures and video segments) and “dynamic resources” (real-time user data and interactive elements crucial for user interactions). A primary challenge in XR environments is optimizing the delivery and caching of these resources within existing network infrastructures to enhance the Quality of Experience (QoE) for users. We introduce a novel hybrid network architecture that integrates resource caching, user-to-user communication, and central server oversight. This architecture not only ensures reliable delivery but also significantly reduces communication latency. Preliminary experiments, conducted under conditions where each node in the network has a 10% chance of failing at any given time, demonstrate that our approach enhances the delivery efficiency of static resources by 68%, affecting 38% of communications, with an increase in latency observed in 4% of cases by 22%. For dynamic resources, it reduces latency in 89% of the cases by an average of 30%, though 8% of cases experienced a 36% increase in latency. These results affirm the effectiveness of our architecture in enhancing user experience in XR environments under challenging network conditions.

Smart cities (SCs), as a novel solution built on top of large-scale Internet of Things (IoT) systems, experiences a rapid growth worldwide, in which, a synergetic orchestration among objects, data, and services is emphasized for innovative solutions to elevate the intelligence of cities based on the fusion of multisource and multimodal data. To enable such orchestration, this article proposes a smart service orchestration architecture (SSOA) to coordinate ubiquitous objects, create interlinked data, and implement versatile smart services. As a proof of concept of SSOA, an informed design platform (IDP) is presented to demonstrate how a smart service system can be designed and how a synergetic orchestration can be implemented to support an informed place design. Moreover, two dedicated mechanisms, namely, place utilization analysis mechanism (PUAM) and ensemble-based activity detection mechanism (EADM), are implemented in a multisource data processing flow to illustrate how massive geo-referenced data can be analyzed effectively and efficiently by machine learning algorithms to extract key information for a comprehensive data fusion required in using multimodal IoT systems. As evaluated, PUAM running in a distributed environment can dramatically improve the performance of geospatial clustering about 11 times from the baseline 53.6 to 4.7 s, and EADM with an ensemble activity classifier achieves the highest accuracy about 87.7% and also the highest f-score per activity category. Finally, various insights about the project testbed Jurong East, Singapore, are discussed to reveal its place design context.

The intelligence of Smart Cities (SC) is represented by its ability in collecting, managing, integrating, analyzing, and mining multi-source data for valuable insights. In order to harness multi-source data for an informed place design, this paper presents “Public Sentiments and Activities in Places” multi-source data analysis flow (PSAP) in an Informed Design Platform (IDP). In terms of key contributions, PSAP implements 1) an Interconnected Data Model (IDM) to manage multi-source data independently and integrally, 2) an efficient and effective data mining mechanism based on multi-dimension and multi-measure queries (MMQs), and 3) concurrent data processing cascades with Sentiments in Places Analysis Mechanism (SPAM) and Activities in Places Analysis Mechanism (APAM), to fuse social network data with other data on public sentiment and activity comprehensively. As proved by a holistic evaluation, both SPAM and APAM outperform compared methods. Specifically, SPAM improves its classification accuracy gradually and significantly from 72.37 to about 85 percent within nine crowd-calibration cycles, and APAM with an ensemble classifier achieves the highest precision of 92.13 percent, which is approximately 13 percent higher than the second best method. Finally, by applying MMQs on “Sentiment&Activity Linked Data”, various place design insights of our testbed are mined to improve its livability.

This paper proposes a method for predicting the performance interference of applications in the virtualized environment. In this method, we firstly analyze the relationship between the performance interference degree and the system-level workloads, and based on this we propose a linear regression algorithm to model relationship between the performance interference degree and the system-level workloads by using the historical data about performance interference degree as the training data set. For the applications without historical data about performance interference degree, we develop a method for predicting the performance interference by clustering the available models of performance interference and matchmaking between the workload pattern of the application and the workload patterns of the available models to generate the performance interference model for the application whose performance interference will to be predicted. By use of the available model, the performance interference of the application can be predicted without historical data about the performance interference among the applications co-located on the same physical host. The experiments show the effectiveness of the proposed measurement and prediction methods of the performance interference among the virtual machines.