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The recent development of unmanned aerial vehicle (UAV) technology has shown the possibility of using UAVs in many research and industrial fields. One of them is for UAVs moving in swarms to provide wireless networks in environments where there is no network infrastructure. Although this method has the advantage of being able to provide a network quickly and at a low cost, it may cause scalability problems in multi-hop connectivity and UAV control when trying to cover a large area. Therefore, as more UAVs are used to form drone networks, the problem of efficiently controlling the network topology must be solved. To solve this problem, we propose a topology control system for drone networks, which analyzes relative positions among UAVs within a swarm, then optimizes connectivity among them in perspective of both interference and energy consumption, and finally reshapes a logical structure of drone networks by choosing neighbors per UAV and mapping data flows over them. The most important function in the scheme is the connectivity optimization because it should be adaptively conducted according to the dynamically changing complex network conditions, which includes network characteristics such as user density and UAV characteristics such as power consumption. Since neither a simple mathematical framework nor a network simulation tool for optimization can be a solution, we need to resort to reinforcement learning, specifically DDPG, with which each UAV can adjust its connectivity to other drones. In addition, the proposed system minimizes the learning time by flexibly changing the number of steps used for parameter learning according to the deployment of new UAVs. The performance of the proposed system was verified through simulation experiments and theoretical analysis on various topologies consisting of multiple UAVs.

PurposeIn this paper, the authors take an amorphous flattened air-ground wireless self-assembling network system as the research object and focus on solving the wireless self-assembling network topology instability problem caused by unknown control communication faults during the operation of this system.Design/methodology/approachIn the paper, the authors propose a neural network-based direct robust adaptive non-fragile fault-tolerant control algorithm suitable for the air-ground integrated wireless ad hoc network integrated system.FindingsThe simulation results show that the system eventually tends to be asymptotically stable, and the estimation error asymptotically tends to zero with the feedback adjustment of the designed controller. The system as a whole has good fault tolerance performance and autonomous learning approximation performance. The experimental results show that the wireless self-assembled network topology has good stability performance and can change flexibly and adaptively with scene changes. The stability performance of the wireless self-assembled network topology is improved by 66.7% at maximum.Research limitations/implicationsThe research results may lack generalisability because of the chosen research approach. Therefore, researchers are encouraged to test the proposed propositions further.Originality/valueThis paper designs a direct, robust, non-fragile adaptive neural network fault-tolerant controller based on the Lyapunov stability principle and neural network learning capability. By directly optimizing the feedback matrix K to approximate the robust fault-tolerant correction factor, the neural network adaptive adjustment factor enables the system as a whole to resist unknown control and communication failures during operation, thus achieving the goal of stable wireless self-assembled network topology.

In wireless sensor network, a connected dominating set (CDS) can be used as a virtual backbone for efficient routing. Constructing a minimal CDS (MCDS) is good for packet routing and energy efficiency, but is an NP-hard problem. In this article, an efficient approximation MCDS construction algorithm E-MCDS (energy efficient MCDS construction algorithm) is proposed which explicitly takes energy consumption into account. E-MCDS contains two stages: the CDS construction stage and the pruning stage. The constructed CDS is approximately composed of two independent sets (IS). The performance ratio of E-MCDS is analysed in both unit disk graph and disk graphs with bidirectional links, being 9.33 opt and 17.33 n k opt , respectively. The message complexity of E-MCDS is O( n ). The simulation results have shown that E-MCDS performs well both in terms of the size of CDS constructed and the energy efficiency.

Multi-radio multi-channel (MRMC) wireless mesh networks (WMNs) achieve higher throughput using multiple simultaneous transmissions and receptions. However, due to limited number of non-overlapping channels, such networks suffer from co-channel interference, which degrades their performance. To mitigate co-channel interference, effective channel assignment algorithms (CAAs) are desired. In this article, we propose a novel CAA, Topology-controlled Interference-aware Channel-assignment Algorithm ( TICA ), for MRMC WMNs. This algorithm uses topology control based on power control to assign channels to multi-radio mesh routers such that co-channel interference is minimized, network throughput is maximized, and network connectivity is guaranteed. We further propose to use two-way interference-range edge coloring, and call the improved algorithm Enhanced TICA ( e-TICA ), which improves the fairness among flows in the network. However, the presence of relatively long links in some topologies leads to conflicting channel assignments due to their high interference range. To address this issue, we propose to utilize minimum spanning tree rooted at the gateway to reduce conflicting channels, and in turn, improve medium access fairness among the mesh nodes. We call the improved algorithm e-TICA version 2 ( e-TICA2 ). We evaluate the performance of the proposed CAAs using simulations in NS2. We show that TICA significantly outperforms the Common Channel Assignment scheme in terms of network throughput, and e-TICA and e-TICA2 achieve better fairness among traffic flows as compared to TICA. It is also shown that e-TICA2 leads to improved network throughput, as compared to TICA and e-TICA.

Minimizing the energy consumption of battery-powered sensors is an essential consideration in sensor network applications, and sleep/wake scheduling mechanism has been proved to an efficient approach to handling this issue. In this article, a coverage-guaranteed distributed sleep/wake scheduling scheme is presented with the purpose of prolonging network lifetime while guaranteeing network coverage. Our scheme divides sensor nodes into clusters based on sensing coverage metrics and allows more than one node in each cluster to keep active simultaneously via a dynamic node selection mechanism. Further, a dynamic refusal scheme is presented to overcome the deadlock problem during cluster merging process, which has not been specially investigated before. The simulation results illustrate that CDSWS outperforms some other existed algorithms in terms of coverage guarantee, algorithm efficiency and energy conservation.

Due to the inefficiency of a flat topology, most wireless sensor networks (WSNs) have a cluster or tree structure; but this causes an imbalance of residual energy between nodes, which gets worse over time as nodes become defunct and replacements are inserted. Multiple layers are better then the typical two-layer cluster-based topology, because it can better accommodate nodes with different levels of residual energy. We propose that each node should periodically determine its own layer, as its situation and the network topology changes. We introduce a topology control scheme for long-term WSNs with these features. Simulations show that this scheme can balance node energy levels, and thus extend network lifetime.

Topology control protocols have been proposed to construct efficient network topologies with several design goals, e.g., network-wide connectivity, minimal energy cost, symmetry, lower nodal degree, and therefore higher spatial reuse or lower interferences. Neighbor-based topology control protocols are simple and assume that each node in the network is connected to its k least-distant neighbors. There have been several empirical and theoretical research efforts that recommend a network-wide optimal value of the local parameter k . However, since most of the design goals often run against each other the suggested lower and upper bounds on the values of k are not sufficient to provide a controllable trade-off among various design goals. In this article, an adaptive neighbor-based topology control protocol is presented where the neighboring nodes collaborate and provide feedback on the network connectivity to decide on their respective transmission ranges. Since every node adaptively adjusts its number of neighbors, the parameter k acts as a performance knob to choose a set of backbone nodes and to form a hierarchical topology structure consisting of symmetric links. Through extensive simulation-based study, it is shown that the value of k can be tuned to generate fully connected network topologies while offering an efficient trade-off among various design goals.

Wireless mesh network (WMN) is a key technology for supporting a variety of application scenarios. Recently, it evolves toward a multi-radio multi-channel (MR-MC) WMN architecture, which can improve network performance by equipping each node with multiple radio interfaces and by using multiple non-overlapping channels. This evolution poses new challenges on network design. Specifically, topology control (TC), one of the fundamental research topics in WMNs, has also received extensive attention in MR-MC WMNs. This article presents an overview of TC mechanisms in the existing literature with emphasis on the mutual dependence of TC on other networking issues such as power control, channel assignment, routing, and directional antennas.

Routing and topology control for Wireless Sensor Networks (WSNs) is significantly important to achieve energy efficiency in resource-constrained WSNs, and high-speed packet delivery. In this article, we introduce a framework for WSN that combines three design approaches: (1) clustering, (2) routing, and (3) topology control. In this framework, we implement an energy-efficient zone-based topology and routing protocol. The framework features a new set of graphs referred to as the Mini Gabriel (MG) graphs. The simulation results demonstrate that the framework with the MG graphs and without these graphs are generally 28% better than the framework with an existing geometric graph. This is in terms of the total network energy consumptions. In addition, the proposed framework is 10, 25, 26, and 46% better than the proposed work with an existing geometric graph in terms of the end-to-end data transmission delay, the transmission energy consumptions, the number of hops in established paths and the routing delay, respectively. Moreover, the MG demonstrates that it achieves the connectivity property, which is critical for WSNs.

In this article, we propose a low-complexity joint power allocation and route planning algorithm for multiple antennas wireless sensor networks using dynamic programming. The sensor nodes utilize orthogonal space time block codes with demodulation-and-forward protocol. Unlike the previous work which typically optimize all the parameters, we cast this Quality-of-Service aware packet forwarding problem into two disjoint procedures: dynamic programming based route planning and subsequent adaptive power allocation. Simulation results indicate that the proposed protocol obtains comparative performance with the optimal results and significantly outperforms classical routing algorithms.