Over the past few years, the use of wireless sensor networks in a range of Internet of Things (IoT) scenarios has grown in popularity. Since IoT sensor devices have restricted battery power, a proper IoT data aggregation approach is crucial to prolong the network lifetime. To this end, current approaches typically form a virtual aggregation backbone based on a connected dominating set or maximal independent set to utilize independent transmissions of dominators. However, they usually have a fairly long aggregation delay because the dominators become bottlenecks for receiving data from all dominatees. The problem of time-efficient data aggregation in multichannel duty-cycled IoT sensor networks is analyzed in this paper. We propose a novel aggregation approach, named LInk-delay-aware REinforcement (LIRE), leveraging active slots of sensors to explore a routing structure with pipeline links, then scheduling all transmissions in a bottom-up manner. The reinforcement schedule accelerates the aggregation by exploiting unused channels and time slots left off at every scheduling round. LIRE is evaluated in a variety of simulation scenarios through theoretical analysis and performance comparisons with a state-of-the-art scheme. The simulation results show that LIRE reduces more than 80% aggregation delay compared to the existing scheme.

Fast data aggregation is crucial for facilitating critical Internet of Things services as it enables the collection of sensory data within strict volume and time constraints. Over the past decades, the data aggregation scheduling problem for minimum latency has garnered significant research attention. Existing approaches to this problem typically schedule all data transmissions based on an aggregation tree, which is constructed without secondary interference. However, such interference can introduce delays when scheduling a transmission from a node to its parent in the tree. To this end, this study proposes an approach called active neighbor exploitation (ANEX) that enables sensor nodes to switch their parents by identifying active neighbors for potential connectivity, irrespective of the receivers established in the tree. Additionally, the scheme prioritizes scheduling nodes with the fewest unscheduled active neighbors, thereby allowing for more concurrent transmissions. ANEX is evaluated through theoretical analysis and extensive simulations under various scenarios. The results demonstrate that ANEX achieves up to 86% faster aggregation compared to the state-of-the-art approach while maintaining an equivalent time complexity.

Over the past few years, the use of wireless sensor networks in a range of Internet of Things (IoT) scenarios has grown in popularity. Since IoT sensor devices have restricted battery power, a proper IoT data aggregation approach is crucial to prolong the network lifetime. To this end, current approaches typically form a virtual aggregation backbone based on a connected dominating set or maximal independent set to utilize independent transmissions of dominators. However, they usually have a fairly long aggregation delay because the dominators become bottlenecks for receiving data from all dominatees. The problem of time-efficient data aggregation in multichannel duty-cycled IoT sensor networks is analyzed in this paper. We propose a novel aggregation approach, named LInk-delay-aware REinforcement (LIRE), leveraging active slots of sensors to explore a routing structure with pipeline links, then scheduling all transmissions in a bottom-up manner. The reinforcement schedule accelerates the aggregation by exploiting unused channels and time slots left off at every scheduling round. LIRE is evaluated in a variety of simulation scenarios through theoretical analysis and performance comparisons with a state-of-the-art scheme. The simulation results show that LIRE reduces more than 80% aggregation delay compared to the existing scheme.

In the rapidly evolving landscapes of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT), the integration of deep learning techniques into routing structures is emerging as a significant advancement. WSNs and IoT systems are pivotal in real-time monitoring and control across various sectors, including environmental monitoring, industrial automation, healthcare, smart cities, and more. These networks rely heavily on efficient data collection and seamless data exchange, facilitated by interconnected nodes and diverse devices. Traditional heuristic-based routing protocols in these networks have focused on optimizing energy consumption, reducing latency, ensuring reliable communication, and maintaining scalability. However, their limited adaptability to dynamic network conditions presents a challenge. The incorporation of deep learning into routing structures offers a solution. By learning and adjusting autonomously in response to changing conditions, deep learning-based routing can dynamically optimize decision-making. This leads to enhanced adaptability, efficiency, and performance, particularly in complex network environments. We propose a paradigm shift from heuristic to deep learning-driven routing structures, emphasizing the potential for improved network management and operational longevity in WSNs and IoT ecosystems.

Efficient broadcast scheduling is a fundamental operation in Wireless Sensor Networks ( WSNs ) for disseminating control messages, software updates, and routing information. However, minimizing network-wide broadcast latency while effectively managing wireless interference and collisions is a known NP-hard problem. Existing heuristic and deterministic approaches often converge to local optima, resulting in sub-optimal transmission delays. In this paper, we propose a novel receiver-seed guided hybrid reinforcement learning ( RL ) -based broadcast scheduling scheme that leverages Q-learning and heuristic sender-receiver matching method to minimize broadcast latency. We formulate the scheduling process as a Markov decision process, where the state represents the progressively expanding set of covered nodes. At each time slot, the RL agent selects a seed action, which is expanded by a collision-aware greedy mechanism into a maximal feasible set of concurrent transmissions. To mitigate the Q-table explosion, we propose an action aggregation method that measures the equivalence of seed actions given a network state, which helps in both reducing redundant Q-table entries and policy convergence. We further accelerate reward propagation along scheduling sequence using the eligibility traces mechanism to immediately reinforce best found scheduling sequence reward. Extensive simulations across diverse network sizes and topologies show that the proposed Q-learning framework achieves lower broadcast delay and higher convergence reliability than benchmarking schemes.