To ensure robustness in wireless networks, monitoring the network state, performance and functioning of the nodes and links is crucial, especially for critical applications. This paper targets Internet of Things (IoT) networks. In the IoT, devices (things) are vulnerable due to security risks from the Internet. Moreover, they are resource-constrained and connected via Lossy links. This work addresses the optimized scheduling of the monitoring role between the embedded devices in IoT networks. The objective is to minimize energy consumption and communication overhead of monitoring, for each node. Several subsets of the potential monitoring nodes are generated by solving a minimal Vertex Cover Problem with Constraint Generation. Assuming periodical functioning, Vertex Covers are optimally assigned to time periods in order to distribute the monitoring role throughout the entire network. The assignment of Vertex Covers to periods is modeled as a Multi-objective Generalized Assignment Problem. To further optimize the energy consumption of the monitors, they are sequenced across time periods to minimize the state transitions of nodes. This part of the problem is modeled as a Traveling Salesman Path Problem. The proposed model is tested on randomly generated instances and the experimental results illustrate its effectiveness to optimize the scheduled monitoring for fault tolerance in IoT networks.

This work is fulfilled in the context of the optimized monitoring of Internet of Things (IoT) networks. IoT networks are faulty; Things are resource-constrained in terms of energy and computational capabilities; they are also connected via lossy links. For IoT systems performing a critical mission, it is crucial to ensure connectivity, availability, and network reliability, which requires proactive network monitoring. The idea is to oversee the network state and functioning of the nodes and links; to ensure the early detection of faults and decrease node-unreachability times. It is imperative to minimize the resulting monitoring energy consumption to allow the IoT network to perform its functions. Furthermore, to realize the integration of the monitoring mechanism with IoT services, this latter should work in tandem with the IoT standardized protocols, especially the IPv6 for Low-power Wireless Personal Area Networks (6LoWPAN) and the Routing Protocol for Low-power and lossy networks (RPL). In this paper, an optimized, proactive, passive monitoring system is proposed for IoT networks. The proposition ensures the optimal placement of monitoring nodes (monitors). Leveraging the graph built by RPL for routing (the DODAG), minimal sets of monitors are optimally placed to cover a given domain. The monitoring activity is optimally scheduled between several subsets of nodes to prolong longevity while minimizing the energy consumption for monitoring, communication, and state transitions. Our proposition provides the exact solution to the defined monitoring placement and scheduling problem via a Binary Integer Program. The model serves as a benchmark for the performance evaluation of contemporary models. Experimentation is designed using network instances of different topologies. Results demonstrate the proposed model’s effectiveness in realizing full monitoring coverage with minimum energy consumption and communication overhead and a balanced distributed monitoring role.

In Wireless Sensor Networks (WSNs) used for real-time applications, ensuring Quality of Service (QoS) is essential for maintaining end-to-end performance guarantees. QoS requirements are typically defined by a set of end-to-end constraints, including delay, jitter, and packet loss. In multi-hop scenarios, this requires multi-constrained path computation. This research examines the standard Routing Protocol for Low-Power and Lossy Networks (RPL), which employs a Destination-Oriented Directed Acyclic Graph (DODAG) for data transmission. Nonetheless, there are several challenges related to multi-constrained route computation in the RPL: (1) The DODAG originates from an objective function that cannot manage multiple constraints. (2) The process of computing multi-constrained routes is resource-intensive, even for a single path. (3) The collection of QoS-compatible paths does not necessarily form a DODAG. To address these challenges, this paper suggests modifications to the existing protocols that shift computationally demanding tasks to edge servers. Such a strategic adjustment allows for the implementation of QoS-compatible route computation in WSNs using the RPL. It enhances their ability to meet increasingly stringent demands for QoS in numerous application environments.

This paper addresses a fundamental challenge in statistical radar detection systems: optimizing the trade-off between the probability of a false alarm ($P_{FA}$) and the probability of a miss ($P_M$). These two metrics are inversely related and critical for performance evaluation. Traditional detection approaches often enhance one aspect at the expense of the other, limiting their practical applicability. To overcome this limitation, a fuzzy hypothesis testing framework is introduced that improves decision making under uncertainty by incorporating both crisp and fuzzy data representations. The methodology is divided into three phases. In the first phase, we reduce the probability of false alarm $P_{FA}$ while maintaining a constant probability of miss $P_M$ using crisp data characterized by deterministic values and classical statistical thresholds. In the second phase, the inverse scenario is considered: minimizing $P_M$ while keeping $P_{FA}$ fixed. This is achieved through parameter tuning and refined threshold calibration. In the third phase, a strategy is developed to simultaneously enhance both $P_{FA}$ and $P_M$, despite their inverse correlation, by adopting adaptive decision rules. To further strengthen system adaptability, fuzzy data are introduced, which effectively model imprecision and ambiguity. This enhances robustness, particularly in scenarios where rapid and accurate classification is essential. The proposed methods are validated through both real and synthetic simulations of radar measurements, demonstrating their ability to enhance detection reliability across diverse conditions. The findings confirm the applicability of fuzzy hypothesis testing for modern radar systems in both civilian and military contexts, providing a statistically sound and operationally applicable approach for reducing detection errors and optimizing system performance.

The construction of partial minimum spanning trees is an NP-hard problem, leading to the development of various heuristic algorithms. Existing heuristics, including Kruskal’s algorithm, frequently employ shortest paths to connect tree components. This study introduces an approximate algorithm for constructing the minimum Steiner tree, which serves as the optimal structure for diffusion multicast. The proposed approach utilizes graph-based structures that provide advantages over conventional shortest-path methods. The algorithm incorporates connections analogous to those in simple Steiner trees when required. These simple trees are represented by hyperedges, and a Hyper Metric Closure can also be applied. Experimental results indicate that this hypergraph-based method enables constructions that more closely approximate the optimal Steiner tree cost compared to traditional pairwise techniques, offering a scalable balance between computational complexity and routing efficiency.

Fairness is critical in delay-sensitive group applications—multiplayer online games, live collaborative editing, and distributed interactive simulations—where every participant should receive each message within a bounded delay and with minimal timing differences between recipients. We study fairness-aware multicast routing under two parameters: the maximum end-to-end delay ($\Delta$) and the inter-destination delay variation ($\delta$). Although several heuristics address this NP-hard problem and exact integer linear programs (ILPs) exist for the minimum-cost multi-constrained case, exact methods that directly optimize inter-destination delay variation under bounded delay remain underexplored. We present flow-based ILP formulations that treat $\Delta$ and $\delta$ as either objectives or constraints. Their feasible solutions are partial spanning hierarchies, a class that contains partial spanning trees as a special case; consequently a hierarchy optimum is, by construction, at least as good as the best tree-constrained solution. On proven-optimal instances, hierarchy optima reduce inter-destination delay variation considerably relative to the best tree-constrained solution. A sensitivity analysis, a real-topology study on the Abilene backbone, and an exact-ILP scalability study quantify and corroborate these gains.