In practical mobile crowdsensing (MCS) systems, many cooperative sensing tasks require a group of reliable participants to perform collaboratively. In this article, we address the problem of group formulation in MCS, which aims to recruit highly trusted participants and form a high-reputation group. We propose a novel Privacy-preserving Trust-Aware Group Formation (PTAGF) framework that ensures trust and privacy between the group members. This framework consists of three mechanisms; the member trust assessment mechanism, the group forming mechanism, and the two-layer privacy-preserving mechanism. Furthermore, we prove that the group forming problem is NP-hard, and thus propose a heuristic-based Trust-Aware Group Formulation (TAGF) algorithm. A theoretical analysis is provided, which demonstrates that the proposed framework achieves privacy and security. Finally, we experimentally evaluate the performance of PTAGF on a real-world dataset against two state-of-the-art approaches. The results show that PTAGF outperforms these approaches in terms of trustworthiness in group selection. Moreover, it achieves reasonable task coverage and running time with different communities size, group sizes, and task scales.

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With the proliferation of mobile crowdsensing (MCS) and crowdsourcing, new challenges are emerging every day. Although crowdsensing has become a popular sensing paradigm to aggregate sensor readings from a variety of sources, data inconsistency has arisen as a serious challenge. Truth discovery (TD) has been developed as an effective method for reducing data inconsistency and as a validity assessment for conflicting data from various sources. In addition, MCS applications and services are moving beyond a single individual participant to community groups and are influenced by group behavior. To address these challenges in this paper, we propose a novel Fog-assisted Group-based Truth Discovery Framework over MCS Data Streams, an efficient TD system for real-time applications. Specifically, we first initialized the weights for the weight update process in TD with the participants’ credibility level. Then, we developed a novel Two-layer Group-based Truth Discovery (TGTD) mechanism in which the first layer estimates the truth of the group’s members and the second layer estimates the aggregated truth for the groups. We have conducted extensive experiments over synthetic and real-world datasets to prove the effectiveness and efficiency of our framework. The results indicate that TGTD achieves superior truth discovery accuracy compared to current streaming truth discovery approaches, while maintaining a reasonable running time. The organization of the streaming process within the fog architecture simulation is identified as an area for further investigation and future work.

Mega low Earth orbit (LEO) satellite constellations are a key infrastructure for 6G global connectivity, but their high node density, fast orbital motion, and continuously changing inter-satellite topology make routing difficult. Clustering is widely used to reduce routing complexity by partitioning a constellation into routing domains. This paper evaluates, side by side, two quantum-ready clustering approaches for mega LEO constellations. The first is qc-kmeans, a hybrid quantum–classical compressive K-means algorithm that selects cluster centroids by solving small quadratic unconstrained binary optimization (QUBO) problems with the quantum approximate optimization algorithm (QAOA). The second, RS-QUBO, partitions the inter-satellite link (ISL) graph directly with a routing- and stability-aware QUBO that rewards compact, balanced routing domains and penalizes changes between consecutive snapshots. Both are compared with classical baselines under identical inputs, with every trial recorded. For qc-kmeans, selecting the best of repeated trials suggests better cluster quality than K-means, but over all trials the two are practically identical on a 10,687-satellite Starlink snapshot and on a synthetic benchmark; an ablation shows that the outcome is determined by the classical K-means seeding and refinement, while QAOA accounts for most of the runtime. For RS-QUBO, on a real, irregular 1454-satellite Starlink sub-network with time-varying links and 3% random link failures, it is the only method of the main comparison that keeps all routes inside their routing domains at every tested number of domains (0% routing failures, compared with 39–71% for K-means, 16–44% for a grid tiling, and 2–22% for recomputed METIS), with a delay stretch of 1.08–1.18 over the routed pairs and 4–11% of satellites changing domain between snapshots. A component ablation attributes the absence of failures to adapting the partition to the current links and repairing disconnected domains, which also removes the failures of recomputed METIS; the compactness objective lowers the delay stretch relative to cut-based partitioning in the recomputed pipelines at 8 and 32 domains but not at 16. Domain changes are costly for routing: under a link-state update model, RS-QUBO needs 0.6–1.2 times the update load of flat routing, mostly to announce satellites that change domain, whereas an incremental refinement of a static METIS partition is also free of routing failures and needs 0.3–0.4 times the flat load. QAOA solves 6–12-satellite instances of this QUBO better than random sampling in ideal simulation, but the benefit becomes marginal under an ibm_torino noise model, and the compiled circuits require up to 960 two-qubit gates. We conclude that, in the evaluated conditions, the benefit of quantum-ready clustering for satellite routing comes from the problem formulation and the classical pipeline around it, solved classically, and that domain stability matters as much for routing as domain quality; QAOA is demonstrated at small scale on current hardware and noise models.

Real-time wireless sensor network (WSN) technology and applications increasingly becoming integral to numerous aspects of daily life, driving the demand for advanced artificial intelligence (AI) technologies that can replace traditional methods in this field. These AI technologies use a variety of techniques, including machine learning (ML), neural networks (NNs), deep learning (DL), fuzzy logic, swarm intelligence, and more. This survey presents current state-of-the-art research on the primary challenges related to WSNs, focusing on contributions, methodologies, strengths, and limitations. Key interrelated areas of investigation include node localization, data collection, aggregation and dissemination, monitoring, and security within WSNs. The findings demonstrate the potential of AI techniques to significantly improve and optimize the efficiency, accuracy, and robustness of WSN applications and operations. Several challenges and open research directions remain, including reducing transmission power, minimizing computational complexity, and ensuring adaptability to various network topologies.