Power allocation scheme for distributed filtering over wireless sensor networks

Abstract

In this work, distributed filtering over wireless sensor networks with limited energy is considered, where each sensor sends its local state estimate to its adjacent sensors through an unreliable wireless channel, which introduces random data packet drops. The packet drop rate depends on the power allocated by the sensor under an energy constraint. Several offline power scheduling strategies are introduced to distribute the power of sensors. A sufficient condition is provided to guarantee the convergence of network estimation error covariance. Further, an online power scheduling strategy is proposed, where each sensor utilises its real-time information to distribute energy for communications. The filtering performance of different power scheduling strategies are compared to show the influence of power distributed scheme on the expected state estimation error covariance.

Distributed fuzzy filtering for load frequency control of non‐linear interconnected power systems under cyber‐physical attacks

“…One critical challenge in traditional power systems is the collection of numerous real-time measurements over wide geographical areas. To solve this challenge, the wireless sensor network (WSN) is seen as a promising technology [3,4]. In general, a WSN contains plentiful sensor nodes that are dispersedly allocated in required working spot.…”

Optimal energy allocation for remote state estimate under Denial‐of‐Service attack

“…By introducing the signal-to-interferenceplus-noise ratio (SINR) model, countermeasures against DoS attacks were presented in [17]. In [18,19], the issue on how to schedule the energy-limited sensors was studied in a distributed framework.…”

Distributed filtering for time-varying networked systems with sensor gain degradation and energy constraint: a centralized finite-time communication protocol scheme

“…Therefore, proper sensor scheduling is still important and is often subject to communication power consumption and battery lifetime for sensor networked systems. Various studies have investigated scheduling techniques [8][9][10][11][12][13][14][15][16][17][18][19][20]. The effective energy-efficient methodologies can be mainly categorized into three types.…”

“…(1) Stochastic scheduling [8][9][10]: each sensor node sends data to a remote estimator according to a given probability at each time step, and an optimal probability distribution is obtained by solving an optimization problem. (2) Deterministic scheduling [11][12][13][14][15]: the sensor node activation sequence is fixed and indicates the moment in which to send data. In [11], the design and performance evaluation of a communication protocol were presented for distributed sensor systems with dynamic and limited energy.…”