Nowadays, Cognitive Radio Sensor Networks (CRSN) arise as an emergent technology to deal with the spectrum scarcity issue and the focus is on devising novel energy-efficient solutions. In static CRSN, where nodes have spatial fixed positions, several reported solutions are implemented via sensor selection strategies to reduce consumed energy during cooperative spectrum sensing. However, energy-efficient solutions for dynamic CRSN, where nodes are able to change their spatial positions due to their movement, are nearly reported despite today’s growing applications of mobile networks. This paper investigates a novel framework to optimally predict energy consumption in cooperative spectrum sensing tasks, considering node mobility patterns suitable to model dynamic CRSN. A solution based on the Kataoka criterion is presented, that allows to minimize the consumed energy. It accurately estimates -with a given probability-the spent energy on the network, then to derive an optimal energy-efficient solution. An algorithm of reduced-complexity is also implemented to determine the total number of active nodes improving the exhaustive search method. Proper performance of the proposed strategy is illustrated by extensive simulation results for pico-cells and femto-cells in dynamic scenarios.

The IoT enables the interconnection of multiple devices, typically sharing network resources. These devices must identify a suitable time to access the channel without interfering with each other, which can lead to additional energy consumption. To extend the network lifetime, cooperative strategies have been proposed that modify the device operations between the ON/OFF states to conserve energy. However, the challenge of selecting active devices for spectrum sensing increases with mobile agents, referred to as IoMT, since their positions may be unknown. To deal with this uncertainty, we propose using the OWA operator, which provides a framework for decision-making under ignorance, to model the position uncertainty resulting from node movement. We estimate node positions by assigning representative values for their distances to the fusion center and primary user. We then determine the optimal number of active nodes that minimize energy consumption while meeting detection constraints. We evaluate performance for different scenarios in networks of various sizes consistent with smart agriculture environments, employing optimistic and pessimistic approaches. The quality of decisions is validated under the assumption of nodes governed by particular mobility patterns.

Filter design with short transient state is a problem encountered in many fields of circuits, systems, and signal processing. In this paper, a novel low-pass filter design technique with time-varying parameters is introduced in order to minimize the rise-time parameter. Through the use of calculus of variations a method is developed to obtain the optimal closed-form expression for adjusting the parameters. In this context, two cases are addressed: the ideal case in which infinite bandwidth is required and a solution of finite bandwidth. The latter is obtained by means of a proper constraint formulation in the frequency domain. The proposed filter achieves the shortest rise time and allows better preservation of the edge shape in comparison with other existing filtering methods. The analysis, synthesis, and performance of the proposed system are discussed and illustrated with the aid of simulations.

The evolution of beyond fifth generation (B5G) wireless networks poses significant technical and economic challenges across urban, suburban, and rural areas, demanding increased capacity for users whose positions continually change. This study investigated the dynamic positioning of an unmanned aerial vehicle (UAV), acting as a mobile base station (MoBS) to enhance network efficiency and meet ground terminals (GTs) expectations for data rates, particularly in emergency scenarios or temporary events. While UAVs show great promise, existing research often assumes certainty in network architecture, overlooking the complexities of unpredictable user movements. We introduce a decision-making framework utilizing the ordered weighted averaging (OWA) operator to address uncertainties in GT locations, enabling the optimization of UAV trajectories to maximize the overall network data rate. An optimization problem is formulated by modeling GT dynamics through a Markov process and discretizing UAV movements while accounting for communication thresholds and movement constraints. Extensive simulations reveal that our approach significantly improves expected data rates by up to 48% compared to traditional fixed base stations (BSs) and predefined UAV movement patterns. This research underscores the potential of UAV-assisted networks to bolster communication reliability while effectively managing dynamic user movements to maintain optimal quality of service (QoS).

Spectrum scarcity in Wireless Sensor Networks (WSN) has been commonly addressed by Cognitive Radio (CR) technology. This has contributed to the emerging paradigm called Cognitive Radio Sensor Networks (CRSN). This work is motivated by the imminent implementation of mobile sensor networks for Internet of Things (IoT). Cooperative Spectrum Sensing (CSS) is most widely used strategy to figure out the hidden terminal problem. In this regard, we determine the number of awake sensors over a dynamic environment to spectrum sensing. A mobility model for sensors is described by means of Markov chains. A first approximation of this kind of scenarios has described based on convex optimization. The proposed solution minimizes the energy consumption subject to constraints on detection performance. The analysis and performance of the proposed system are discussed and illustrated with the aid of simulations.