Owing to the dynamic nature of sensor network applications the adoption of adaptive cluster-based topologies has many untapped desirable benefits for the wireless sensor networks. In this study, the authors explore such possibility and present an adaptive clustering algorithm to increase the network's lifetime while maintaining the required network connectivity. The proposed scheme features capability of cluster heads to adjust their power level to achieve optimal degree and maintain this value throughout the network operation. Under the proposed method a topology control allows an optimal degree, which results in a better distributed sensors and well-balanced clustering system enhancing networks' lifetime. The simulation results show that the proposed clustering algorithm maintains the required degree for inter-cluster connectivity on many more rounds compared with hybrid energy-efficient distributed clustering (HEED), energy-efficient clustering scheme (EECS), low-energy adaptive clustering hierarchy (LEACH) and energy-based LEACH.

Topology-controlled adaptive clustering for uniformity and increased lifetime in wireless sensor networks

In this paper, we propose adaptive multi-objective optimization framework based on non-dominated sorting genetic algorithm-II and learning automata (LA) for coverage and topology control in heterogeneous wireless sensor networks. The multi-objective optimization approach of the proposed framework, called MOOCTC (multi-objective optimization coverage and topology control), can simultaneously optimize several conflicting issues such as number of active sensor nodes, coverage rate of the monitoring area and balanced energy consumption while maintaining the network connectivity. This approach incorporates problem-specific knowledge in its operators to find high-quality solutions. In addition, this approach uses LA to dynamically adapt the crossover and mutation rates without any external control to improve the behavior of the optimization algorithm. Simulation results demonstrate the efficiency of the proposed multi-objective optimization approach in terms of lifetime, coverage and connectivity.

AMOF: adaptive multi-objective optimization framework for coverage and topology control in heterogeneous wireless sensor networks

The Wireless Sensor Networks (WSNs) design related questions give rise to new complex and difficult theoretical problems and challenges in operations research and optimization areas. As WSNs become increasingly pervasive, a good understanding of these problems in terms of theoretical complexity is of great help in designing appropriate algorithms. In this paper, we examine some of the most fundamental optimization problems related to coverage, topology control, scheduling, routing and mobility in WSNs. Then we focus on their complexity and analyze the differences that exist with the counter part conventional theoretical problems or those already studied in traditional networks. We present as well some of the main methods proposed in the literature and report some open issues regarding these problems.

/pdf/optimization-problems-in-wireless-sensor-networks-34j5lx8ywj.pdf

Optimization Problems in Wireless Sensor Networks

Tracking Control for Stochastic Multi-Agent Systems Based on Hybrid Event-Triggered Mechanism

Topology control is an important technique to improve the connectivity and the reliability of Wireless Sensor Networks (WSNs) by means of adjusting the communication range of wireless sensor nodes. In this paper, a novel Fuzzy-logic Topology Control (FTC) is proposed to achieve any desired average node degree by adaptively changing communication range, thus improving the network connectivity, which is the main target of FTC. FTC is a fully localized control algorithm, and does not rely on location information of neighbors. Instead of designing membership functions and if-then rules for fuzzy-logic controller, FTC is constructed from the training data set to facilitate the design process. FTC is proved to be accurate, stable and has short settling time. In order to compare it with other representative localized algorithms (NONE, FLSS, k-Neighbor and LTRT), FTC is evaluated through extensive simulations. The simulation results show that: firstly, similar to k-Neighbor algorithm, FTC is the best to achieve the desired average node degree as node density varies; secondly, FTC is comparable to FLSS and k-Neighbor in terms of energy-efficiency, but is better than LTRT and NONE; thirdly, FTC has the lowest average maximum communication range than other algorithms, which indicates that the most energy-consuming node in the network consumes the lowest power.

/pdf/a-novel-topology-control-approach-to-maintain-the-node-3jqwzh0emn.pdf

A novel topology control approach to maintain the node degree in dynamic wireless sensor networks.

https://www.microsoft.com/en-us/research/wp-content/uploads/2016/12/A-Cooperative-Approach-for-Topology-Control-in.pdf

Stefano Brambilla

Papers

Fast track article: A cooperative approach for topology control in Wireless Sensor Networks