The demand for the digital monitoring of environmental ecosystems is high and growing rapidly as a means of protecting the public and managing the environment. However, before data, algorithms, and models can be mobilized at scale, there are considerable concerns associated with privacy and security that can negatively affect the adoption of technology within this domain. In this paper, we propose the advancement of electronic environmental monitoring through the capability provided by the blockchain. The blockchain’s use of a distributed ledger as its underlying infrastructure is an attractive approach to counter these privacy and security issues, although its performance and ability to manage sensor data must be assessed. We focus on a new distributed ledger technology for the IoT, called IOTA, that is based on a directed acyclic graph. IOTA overcomes the current limitations of the blockchain and offers a data communication protocol called masked authenticated messaging for secure data sharing among Internet of Things (IoT) devices. We show how the application layer employing the data communication protocol, MAM, can support the secure transmission, storage, and retrieval of encrypted environmental sensor data by using an immutable distributed ledger such as that shown in IOTA. Finally, we evaluate, compare, and analyze the performance of the MAM protocol against a non-protocol approach.

Wireless Sensor Networks (WSNs) are crucial in various fields including Health Care Monitoring, Battlefield Surveillance, and Smart Agriculture. However, WSNs are susceptible to malicious attacks due to the massive quantity of sensors within them. Hence, there is a demand for a trust evaluation framework within WSNs to function as a secure system, to identify and isolate malicious or faulty sensor nodes. This information can be leveraged by neighboring nodes, to prevent collaboration in tasks like data aggregation and forwarding. While numerous trust frameworks have been suggested in the literature to assess trust scores and examine the reliability of sensors through direct and indirect communications, implementing these trust evaluation criteria is challenging due to the intricate nature of the trust evaluation process and the limited availability of datasets. This research conducts a novel comparative analysis of three trust management models: “Lightweight Trust Management based on Bayesian and Entropy (LTMBE)”, “Beta-based Trust and Reputation Evaluation System (BTRES)”, and “Lightweight and Dependable Trust System (LDTS)”. To assess the practicality of these trust management models, we compare and examine their performance in multiple scenarios. Additionally, we assess and compare how well the trust management approaches perform in response to two significant cyber-attacks. Based on the experimental comparative analysis, it can be inferred that the LTMBE model is optimal for WSN applications emphasizing high energy efficiency, while the BTRES model is most suitable for WSN applications prioritizing critical security measures. The conducted empirical comparative analysis can act as a benchmark for upcoming research on trust evaluation frameworks for WSNs.

Acoustic sensor nodes protecting logistics sites use fixed-label classifiers, so adding a class requires new data, retraining, and redeployment of every node. Audio–language models promise to add classes by text description. We evaluated three frozen contrastive audio–language models on 25,599 events (15,241 gunshots) and on an external field set, and measured their cost on six edge platforms. On supplied clips, a vocabulary-independent score calibrated on separate data for a 5% false-positive target recalled 79% to 84% of gunshots at realized rates of 4.8% to 5.6% against unseen background sounds. On field recordings from an unseen domain, separation held on full clips (area under the ROC curve 0.95 to 0.97), but the clip-level false-positive rate rose to 7% to 11%, and only 24% to 25% of gunshots were detected end to end because the onset gate missed distant shots. Fine-grained zero-shot classification was weak: balanced accuracy was 0.53 against 0.50 for supersonic versus subsonic fire and 0.05 against 0.04 for firearm identity. A linear probe reached 0.82 at the regime level, but 0.59 with calibers held out. A firearm class added by one sentence reached 6.4% accuracy, against 54% with twenty labeled examples.

Networks of fixed, low-cost dose-rate sensors monitor radiological facilities, but the trustworthiness of their reports rests on heuristics: existing trust schemes score nodes by mutual voting or behavioral statistics, which fail against an adversary who falsifies data consistently with the physics the network can observe. This paper presents PRAHARI, a trust management framework in which an unmanned aerial system carrying a calibrated scintillator chain hovers at each node as a mobile metrological anchor. Trust evidence is a statistically principled compatibility test between two uncertainty-quantified measurements with an empirically verified false-alarm rate, feeding a multi-channel Beta reputation engine with wall-clock forgetting, endurance-constrained verification scheduling, and trust-gated dose mapping. Across 9640 paired Monte Carlo trials on a shielded-facility twin with randomized attack classes, PRAHARI is compared against three published network-internal trust methods, a physics-model residual detector, and an uncertainty-blind aerial band. Only aerial-reference evidence discriminates scaling and physics-consistent falsifiers (class AUC 0.80 and 0.75), while network-internal methods stay near chance (0.48 to 0.60), an advantage persisting across facility variants and up to half the adversary’s shielding knowledge, at a false-accusation rate one order of magnitude below every alternative of comparable ranking power. Sensitivity, ablation, reference-bias, and drift studies bound the claims.