Cluster-Based Information-Centric Wireless Sensor Networks Management for Enhanced User Security Satisfaction in the Internet of Things
Bibliographic record
Abstract
Many Internet of Things applications, such as smart cities and intelligent transportation, require accessible data to users (i.e., data consumers).To address users' timely data access needs, Information-Centric Wireless Sensor Networks (ICWSNs) were proposed that allow users to access data directly from cache nodes.Particularly, ICWSNs are clustered, and Cluster Heads (CHs) are selected to collect data from basic sensing nodes and act as cache nodes.Nevertheless, clustering and ensuring data security in ICWSNs is challenging.This is because sensor nodes are often resource-constrained, heterogeneous (i.e., perform different sensing tasks), and/or mobile.Driven by users' security and timely data access needs, in this thesis, cluster-based ICWSNs' management for enhanced user security satisfaction is investigated.First, a security-aware CHs selection algorithm is proposed to optimize network coverage that is subject to security and energy constraints.Then, cluster-based ICWSNs with heterogeneous communities are modeled analytically and compared to conventional cluster-based ICWSNs with heterogeneous sensor nodes.To overcome the identified energy-latency and security trade-off, a Security Level Aware algorithm for Clusterbased ICWSNs with Heterogeneous communities (SLAC-H) is proposed.In SLAC-H, community leaders collect and forward application-domain-specific data to CHs.Next, Node Embedding with Security Resource Allocation (NESRA) clustering algorithm for mobile ICWSNs is proposed.To improve user security satisfaction, NESRA allocates security resources to sensor nodes based on their location, mobility, and energy resources.Still, when security is set as a priority, many times, more energy is spent on security than is actually required.Therefore, along with the mobility-aware NESRA, User-aware clustering with Security Resource Allocation (USRA) algorithm is proposed.In USRA, a sink node determines which security resource each sensor node will be using for the next round to avoid over-utilization of network resources while satisfying user security needs.A summary of the proposed algorithms and some highlights for future work conclude this thesis. List of Tables4.1 Security levels mapping based on the security mechanism used [65]. .33 5.1 Security resources and their corresponding security levels. . . . . . . .43 5.2 Simulation settings. . . . . . . . . . . . . . . . . . . . . . . . . . . . .60 5.3 Average energy saving percentage for SLAC-H with on average 30 data requests per round and different path loss exponents. . . . . . . . . .62 5.4 Average total percentage of remaining energy during the lifetime of a cluster-based ICWSN for SLAC-H, TCM, and LEACH with heterogeneous communities and SLAC-H without communities with different data request arrival rates. . . . . . . . . . . . . . . . . . . . . . . . .62 5.5 Average total percentage of energy spent on security during the lifetime of a cluster-based ICWSN for SLAC-H, TCM, and LEACH with heterogeneous communities and SLAC-H without communities with different data request arrival rates. . . . . . . . . . . . . . . . . . . .63 5.6 Latency savings of cluster-based ICWSNs with heterogeneous communities compared to the case with no communities. . . . . . . . . . . .69 6.1 Simulation settings. . . . . . . . . . . . . . . . . . . . . . . . . . . . .84 7.1 Simulation settings. . . . . . . . . . . .
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".