Unmanned Aerial Vehicles (UAVs) are a promising solution for sensor data collection (DC) in large-scale area. The challenge is to minimize the DC route, which will reduce UAV energy consumption and data latency. The novelty of this paper lies in its innovative approach to optimizing sensor data collection by UAVs. It combines Ant Colony Optimization (ACO) and K-means algorithms to establish an initial shortest route and introduces a unique method for grouping sensor nodes (SNs) along the route based on the UAV's footprint, reducing data latency and energy consumption for both UAV and sensors. First, an initial shortest route that traverses all SNs is established based on the ACO and the K-means algorithms. Second, we group the sensor nodes (SNs) along the initial route using the footprint of the UAV, so that the latter can collect the data of the group in one stop, instead of stopping at each SN. By sequencing the hovering locations, we obtain a (shorter) intermediate route. Third, we shorten this route even further, by applying ACO to the set of hovering locations of the intermediate route. The solution has been implemented fully in Python. The results show that the route length gets shorter progressively with each phase. To evaluate the performance of the solution objectively, we have compared it with four states of the art solutions. The results show vividly that the proposed solution produces a DC route 19.28% shorter than the shortest route produced by the four competitive solutions. Moreover, it demonstrates a remarkable improvement by retaining 44% of energy in most SNs while over 99% energy depletion observed in the five state-of-the-art competitive solutions.

Ensuring secure and energy-efficient data collection remains a critical challenge in wireless sensor networks (WSNs), where resource-constrained sensor nodes operate in vulnerable and dynamic environments. This article proposes S-EMSDC, a novel two-layer secure and energy-aware cluster-based data collection framework employing a mobile sink. The first layer integrates an Improved Discretized INFO (IDINFO) mobility optimization algorithm to minimize transmission distance and balance energy consumption through optimized sink trajectory planning, adaptive clustering, and dynamic cluster-head (CH) rotation. The second layer eliminates explicit key exchange and strengthens security by combining AES-256–based node authentication with HMAC-SHA256 integrity verification, ensuring early rejection of forged or manipulated data before aggregation. Extensive simulations on networks ranging from 100 to 500 nodes demonstrate the superiority of Secure and Energy-Efficient data collection framework for WSNs employing a Mobile Sink (S-EMSDC) over recent state-of-the-art schemes, including Energy-Efficient Mobile Sink-Based Intelligent Data Routing (EEMSR), Genetic algorithm based sink mobility for energy efficient data routing (GA-SMT), Balanced Inter-cluster and Inner-cluster Energy (BIIE), Group Teaching Algorithm by using the Bald Eagle (GTA-BE), and energy-aware path construction (EAPC). Results show that S-EMSDC extends network lifetime by up to 74%, reduces average energy consumption by over 60% compared to baseline methods and by 20–40% relative to recent protocols such as EA-PC and GA-SMT, depending on the number of rounds. The framework also achieves near-zero successful identity forgery across varying node densities and identifier lengths, confirming its robustness against identity-based attacks. Overall, S-EMSDC provides a secure, scalable, and energy-efficient solution suitable for mission-critical WSN applications.

A large body of research has recently focused on minimizing task latency in mobile edge computing (MEC). Researchers in the field have typically posed the minimization exercise as a non-convex optimization problem and solved it either mathematically or metaheuristically, overlooking the fact that neither provides the optimal solution. Specifically, the mathematical paradigm provides a lower bound on the optimal solution, and the metaheuristic paradigm provides an upper bound. The present work is the first, to the best of our knowledge, to explore this fact and quantify the gap between the two bounds, where the optimal solution actually lies. To this end, we craft a baseline MEC model and formulate a non-convex optimization problem with the objective to minimize task latency. We then define three partitioning vectors, one for the workloads and two for the MEC resources. By searching for the optimal values of these three partitioners, the solution lower bound is obtained mathematically using Lagrange multipliers, and the solution upper bound is obtained metaheuristically twice using two algorithms, genetic and bee colony. The experimental results show that the gap between the two bounds can be as small as $$2\%$$ and as large as $$14\%$$ , depending on the model parameters, reasserting the fact that settling with only one solution paradigm can be dreadfully misleading.