Is Fragmentation a Threat to the Success of the Internet of Things?
Bibliographic record
Abstract
Internet of Things (IoT) aims to bring connectivity to almost every objects, i.e., things, found in the physical space. It extends connectivity to everyday things, however, such increase in the connectivity creates many prominent challenges. Context: Generally, IoT opens the door for new applications for machine-to-machine and human-to-human communications. The current trend of collaborating, distributed teams through the Internet, mobile communications, and autonomous entities, e.g., robots, is the first phase of the IoT to develop and deliver diverse services and applications. However, such collaborations is threatened by the fragmentation that we witness in the industry nowadays as it brings difficulty to integrate the diverse technologies of the various objects found in IoT systems. Diverse technologies induce interoperability issues while designing and developing various services and applications, hence, limiting the possibility of reusing the data, more specifically, the software (including frameworks, firmware, applications programming interfaces, and user interfaces) as well as of facing issues, like security threats and bugs, when developing new services or applications. Different aspects of handling data collection ranging from discovering smart sensors for data collection, integrating and applying reasoning on them must be available to provide interoperability and flexibility to the diverse objects interacting in the system. However, such approaches are bound to be challenged in future IoT scenarios as they bring substantial performance impairments in settings with the very large number of collaborating devices and technologies. Objective: We raise the awareness of the community about the lack of interoperability among technologies developed for IoT and challenges that their integration poses. We also provide guidelines for researchers and practitioners interested in connecting IoT networks and devices to develop services and applications. Method: We apply the methods advocated by the evidence-based software engineering paradigm. This paradigm and its core tool, the systematic literature review (SLR), were introduced to the software-engineering research community early 2004 to help researchers and industry systematically and objectively gather and aggregate evidences about different topics. In this paper, we conduct an SLR of both IoT interoperability issues and the state-of-practice of IoT technologies in the industry, highlighting the integration challenges related to the IoT that have significantly shifted the landscape of Internet-based collaborative services and applications nowadays. Results: Our SLR identifies a number of studies from journals, conferences, and workshops with the highest quality in the field. This SLR reports different trends, including frameworks and technologies, for the IoT for better comprehension of the paradigm and discusses the integration and interoperability challenges across the different layers of this technology while shedding light on the current IoT state-of-practice. It also discusses some future research directions for the community.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.013 | 0.039 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".