Abstract In wireless sensor and actuator networks (WSAN), sensors can have much longer lifetime as mobile sink nodes move through the network and collect data from static sensor nodes. Applying mobile sinks in WSAN systems has faced challenges in sink path planning and data gathering scheduling. Many studies have focused on priori‐trail planning for mobile sinks; however, they have not considered tracking performance degradation during operation due to system nonlinearities and uncertain WSAN environments. Thus, two learning‐based adaptive feedback control methods, adaptive neural‐network‐based feedback control (ANNFC) and adaptive neural‐network‐based feedback control with integrator model (ANNFC‐IM), are proposed to improve and optimise tracking performance in unknown and uncertain WSAN environments. The proposed algorithms enable a mobile sink to adjust its flying trajectory with online learning by applying a neural‐network‐based control method. The performance of the proposed algorithm is validated by theoretical analysis and simulation results. The results demonstrate that the proposed methods significantly outperform existing methods in terms of tracking error, system stability, and convergence.

Smart interference management methods are required to enhance the throughput, coverage, and energy efficiency of a dense small cell network. In this paper, we propose a transmit power control for energy efficient operation of a dense small cell network. We cast the power control problem as a noncooperative game to satisfy the design requirement that small cells do not need any information exchange among them. We analyze the sufficient condition for the existence of a Nash equilibrium (NE) state of the proposed game. We also analyze that the NE state is unique by transforming the original nonlinear fractional programming problem into a nonlinear parametric programming problem. Through simulation studies, we verify our analysis results. In addition, we show that the proposed method achieves higher energy efficiency of a network and balances the energy efficiency among cells more evenly than the methods based on the AIMD (additive increase and multiplicative decrease) algorithm.

Wireless sensor networks (WSNs) generally comprise a large number of tiny sensor nodes that perform network processing of the acquired data and then forwarding such data to the sink via multi‐hop paths. The sensor nodes are resource constrained in terms of battery life, memory, and processing capability. Hence, a critical aspect in WSNs is power scarcity, which directly affects the network operation lifetime and the performance of applications. Furthermore, large‐scale WSNs demand a high level of self‐organisation so each node of the system autonomously makes decisions. In this respect, self‐organising methods enhancing the network lifetime while achieving balanced energy are highly significant in WSNs. In this study, the authors apply gene regulatory network (GRN) principles to WSN system and design a new GRN‐inspired model for autonomous node scheduling in WSNs. GRNs have received considerable attention from computational engineering for their robustness, scalability, and adaptability with simple local interactions and limited information. They apply cellular mechanisms of GRNs to WSNs and establish a metaphor between a multi‐cellular system and a WSN system. Then, they propose a new model inspired by GRN so each sensor node autonomously schedules its state with local interaction based on sensor variable signalling while achieving the global object predefined by an application or user. Using control theory, they analyse system stability and derive steady states of the proposed system. They further derive the conditions of system parameters to ensure system convergence to a desired state. Simulation and numerical results are evaluated to provide insights into the effect of various system parameters on energy balancing and system stability.

In duty-cycled wireless sensor networks running asynchronous MAC protocols, the time when a sender waits for its receiver to wake up and receive the packet is the major source of energy consumption. Opportunistic routing can reduce the sender wait time by allowing multiple candidate receivers, but by doing that it suffers from redundant packet forwarding due to multiple receivers waking up at the same time. Thus, the number of forwarders should be controlled in a way that overall forwarding cost is minimized considering both sender wait time and cost of redundant packet forwarding. Also, in order to prolong network lifetime, candidate forwarders should be selected so that load is balanced among nodes. We propose ORR, an opportunistic routing protocol that addresses the two issues. First, the optimal number of forwarders is calculated based on forwarding cost estimation, which is derived from duty cycle and network topology. Second, the metric used for selecting forwarders considers residual energy so that more traffic is guided through nodes with larger remaining energy. The resulting routing protocol is proven to avoid loops and shown to achieve longer network lifetime compared to other protocols regardless of duty cycle and network topology.

Balancing load among servers is an important research challenge for a large-scale loosely coupled heterogeneous server system (LCHSS), to improve both the total throughput of the system and the quality of service experienced by clients. In practical terms, a load-balancing method for an LCHSS have to drive servers to underloaded states without unnecessary load migrations among servers. To tackle this problem, we propose a load-balancing framework inspired by biological systems that have developed adaptive, robust, and flexible behaviors through the local interactions of individual nodes with limited information. In our framework, we integrate two different biological models systematically and develop new mathematical formulas. With the developed formulas, we introduce two key rules to balance the load levels among servers in a fully distributed manner through the employment of the inter-cell signaling model and the Kuramoto synchronization model. Using a mathematical stability analysis, we provide a guide for the configuration of model parameters and prove that a system stabilizes at a steady state. Using task event logs measured at a Google cluster, we conducted a variety of case studies for the evaluation of the framework. The evaluation results verify that our framework balances the load levels among servers in spite of the variability in a system.

Wireless sensor-actuator networks (WSANs) enhance the existing wireless sensor networks (WSNs) by equipping sensor nodes with actuators. The actuators work with the sensor nodes to perform application-specific operations. The WSAN systems have several applications such as disaster relief, intelligent building management, military surveillance, health monitoring, and infrastructure security. These applications require the capability of fast data dissemination in order to act responsively to events. However, due to strict resource constraints of the nodes, WSANs pose significant challenges in network protocol design to support applications with delay requirements. Biologically inspired modeling techniques have received considerable attention for achieving robustness, scalability, and adaptability, while retaining individual simplicity. Specifically, data dissemination, packet routing, and broadcasting protocols for wireless networks have been modeled by epidemic theory. However, existing bio-inspired algorithms are mostly based on predefined heuristics and fixed parameters, and thus it is difficult for them to achieve the desired level of performance under dynamic environments. In order to solve this problem, we propose an epidemic-inspired algorithm for data dissemination in WSANs which automatically controls node states to meet the delay requirements while minimizing energy consumption. Through mathematical analysis, behavior of the algorithm in terms of converge time and steady state can be predicted. Also, the analysis shows that the system achieves stability, and derives parameter conditions for achieving the stability. Finally, extensive simulation results indicate that the proposed scheme outperforms existing protocols in achieving delay requirements and conserving energy.

This paper proposes a control-based approach to the duty cycle adaptation for wireless sensor networks. The proposed method controls the duty cycle through the queue management to achieve high-performance under variable traffic rates. To have energy efficiency while minimizing the delay, we design a feedback controller, which adapts the sleep time to the traffic change dynamically by constraining the queue length at a predetermined value. In addition, we propose an efficient synchronization scheme using an active pattern, which represents the active time slot schedule for synchronization among sensor nodes, without affecting neighboring schedules. Based on the control theory, we analyze the adaptation behavior of the proposed controller and demonstrate system stability. The simulation results show that the proposed method outperforms existing schemes by achieving more power savings while minimizing the delay.