Control Of Mobile Sensor Networks Research Articles

Coverage control of mobile sensor networks with directional sensing.

Control design of mobile sensors for coverage problem is addressed in this paper. The mobile sensors have non-linear dynamics and directional sensing properties which mean the sensing performance is also affected by the pointing directions of the sensors. Different from the standard optimal coverage problem where sensors are assumed to be omni-directional ones, orientation angles of the directional sensors should also be controlled, other than the position control, to achieve the coverage purpose. Considering also the non-linear dynamics of the mobile sensors, new control methodology is necessarily developed for the coverage problem with directional sensors. In the approach proposed, an innovative gradient based non-smooth motion controller is designed for the mobile sensors with unicycle dynamics. With the proposed controllers, the states of sensors will always stay in an positive invariant set where the gradient of the performance valuation function is well-defined if they are initialized within this set. Moreover, the sensors' states are proved to converge to some critical point where the gradient is zero. Simulation results are provided to illustrate the performance of the proposed coverage control strategy.

Improved design of low energy clustering algorithm for mobile sensor network

LEACH class clustering algorithms have been widely used in the topology control of mobile sensor networks because of their simplicity, practicality and good energy efficiency. Aiming at the common problems of LEACH clustering algorithm, such as large energy consumption of nodes, short network life cycle, easy loss of data transmission, etc., according to the set mobile sensor network model, based on LEACH-Mobile algorithm, a low-energy clustering algorithm LEACH-MII is designed. LEACH-MII clustering algorithm assigns tasks according to the working conditions of sensor nodes, combines the activity, location and energy consumption of nodes to select and rotate cluster heads; and uses dynamic time slot allocation mechanism to avoid time slot reallocation caused by frequent join-cluster and leave-cluster of sensor nodes, so as to ensure the stability and connectivity of the network structure, reduce the energy consumption of nodes, and extend the life cycle of the network. In order to verify the feasibility and effectiveness of LEACH-MII clustering algorithm, the simulation experiment of LEACH-MII clustering algorithm is carried out. The simulation results show that The data transmission efficiency, average node energy consumption and network life cycle of LEACH-MII network are 30%, 20% and 15% better than LEACH-Mobile network, respectively. In addition, the performance of LEACH-MII network is better than that of LEACH-Mobile network, such as the load balance degree, the scalability of network monitoring range, and the speed effect of network life cycle.

Distributed Nash Equilibrium Seeking for Games in Systems With Bounded Control Inputs

Noticing that actuator limitations are ubiquitous in practical engineering systems, this article considers Nash equilibrium seeking for games in systems where the control inputs are bounded. More specifically, first-order integrator-type systems with bounded control inputs are first considered and two saturated control strategies are designed to seek the Nash equilibrium of the game. Then, second-order integrator-type systems are further considered. In this case, a centralized seeking strategy is first proposed without considering the boundedness of the control inputs, followed by a distributed counterpart. By further adapting a saturation function into the distributed Nash equilibrium seeking strategy, the boundedness of the control input is addressed. In the proposed distributed strategies, consensus protocols are included for information sharing and the saturation functions are utilized to construct bounded control inputs. The convergence results are analytically studied by Lyapunov stability analysis. Finally, by considering the connectivity control of mobile sensor networks, the proposed methods are numerically verified.

Mobile Sensor Networks and Control: Adaptive Sampling of Spatiotemporal Processes

The control of mobile sensor networks uses sensor measurements to update a model of an unknown or estimated process, which in turn guides the collection of subsequent measurements—a feedback control framework called adaptive sampling. Applications for adaptive sampling exist in a wide range of settings, especially for unmanned or autonomous vehicles that can be deployed cheaply and in cooperative groups. The dynamics of mobile sensor platforms are often simplified to planar self-propelled particles subject to the ambient flow of the surrounding fluid. Sensor measurements are assimilated into continuous or discrete models of the process of interest, which in general can vary in space and time. The variability of the estimated process is one metric to score future candidate sampling trajectories, along with information- and uncertainty-based metrics. Sampling tasks are allocated to the network using centralized or decentralized optimization, in order to avoid redundant measurements and observational gaps.

Event-Based Communication and Finite-Time Consensus Control of Mobile Sensor Networks for Environmental Monitoring.

This paper deals with the problem of environmental monitoring by designing a cooperative control scheme for mobile sensor networks. The proposed cooperative control scheme includes three main modules: a wireless communication module, a direction decision module, and a motion control module. In the wireless communication module, an event-based communication rule is proposed, which means that mobile sensor nodes do not send their positions, velocities, and the data of environmental attributes to the other sensor nodes in real-time for the coordination and control of mobile sensor networks. Due to using the event-based communication rule, the communication bandwidth can be saved. In the direction decision module, a radial basis function network is used to model the monitored environment and is updated in terms of the sampled environmental data and the environmental data from the other sensor nodes by the wireless communication module. The updated environment model is used to guide the mobile sensor network to move towards the region of interest in order to efficiently model the distribution map of environmental attributes, such as temperature, salinity, and pH values for the monitored environment. In the motion control module, a finite-time consensus control approach is proposed to enable the sensor nodes to quickly change their movement directions in light of the gradient information from the environment model. As a result of using the event-based communication rule in the wireless communication module, the proposed control approach can also lower the updating times of the control signal. In particular, the proposed cooperative control scheme is still efficient under the directed wireless communication situation. Finally, the effectiveness of the proposed cooperative control scheme is illustrated for the problem of environmental monitoring.

Distributed Coverage Control of Mobile Sensor Networks in Unknown Environment Using Game Theory: Algorithms and Experiments

This paper studies the coverage problem in an unknown environment by a Mobile Sensor Network (MSN). Each agent in the MSN has communication, sensing, moving, and computation capabilities to complete sensing tasks. These agents would have some limitations on time and energy to accomplish their tasks that need to be considered by the designers. Here, the agents need to relocate themselves, from their initial random locations, to their optimal configuration. An algorithm based on game theory is proposed, where a collection of distributed agents communicate with local neighbors and use their local information make decisions. A state-based potential game is defined in which each agent's utility function is designed to consider the trade-off between the worth of the covered area and the energy consumption. The agents employ binary log-linear learning to update their actions in each iteration in order to converge to the Nash equilibrium. As the agents do not have the knowledge of the sensing area, a Gaussian Mixture Model (GMM) is used to model the distributions of the worth in the sensing area. To estimate the unknown parameters of the GMM, a Maximum Likelihood (ML) estimation scheme is employed, where an expectation-maximization algorithm is used as a tool to solve the ML recursively. Then, in order to feed the estimation algorithm with more informative data, a mutual information term is added to the agents’ utility functions. The mutual information is utilized to determine which observation can improve the agent's knowledge of the unobserved area more. Both simulation results and experimental results on a multi-robot platform are provided to validate the performance of the proposed algorithm.

Distributed estimation and control of mobile sensor networks based only on position measurements

Most existing distributed estimation and control algorithms for mobile sensor networks rely on the information of both position and velocity of neighbour sensors. The authors' investigate the distributed estimation and control of mobile sensor network with only position measurements. Based on the Kalman-consensus filter and the flocking algorithm, all mobile sensors move to a target, and achieve consensus on the estimations of the target. Under the assumption that the initial interactive network is connected, the stability of the cascading system is proved via Lyapunov stability theory and matrix principle. Moreover, a sufficient stability condition in the form of the boundary conditions of the feedback gain is proposed for the mobile sensor network. Some numerical examples are provided to illustrate the effectiveness of theoretical results.

Distributed tracking control of mobile sensor networks with intermittent communications

This paper investigates the distributed tracking control problem of mobile sensor networks (MSNs) with intermittent communications. The MSNs are assumed to operate in a leader-following structure. When the communication network is accessible, the input of the leader is nonzero and unknown to the followers. When the communication is unavailable, the leader input is assumed to be zero. Based on intermittently available relative state information of neighboring sensors, a distributed tracking control method is proposed for the MSNs. A sufficient condition under which all follower sensors can track the leader under a fixed topology is derived using graph theory, non-smooth analysis and the switching system approach. Furthermore, an interesting design algorithm is proposed by which the tracking controller and the minimum admissible communication rate can be calculated simultaneously. The effectiveness of the results is demonstrated by numerical simulations.

Cooperative Control of Mobile Sensor Networks for Environmental Monitoring: An Event-Triggered Finite-Time Control Scheme.

This paper deals with the problem of environmental monitoring by developing an event-triggered finite-time control scheme for mobile sensor networks. The proposed control scheme can be executed by each sensor node independently and consists of two parts: one part is a finite-time consensus algorithm while the other part is an event-triggered rule. The consensus algorithm is employed to enable the positions and velocities of sensor nodes to quickly track the position and velocity of a virtual leader in finite time. The event-triggered rule is used to reduce the updating frequency of controllers in order to save the computational resources of sensor nodes. Some stability conditions are derived for mobile sensor networks with the proposed control scheme under both a fixed communication topology and a switching communication topology. Finally, simulation results illustrate the effectiveness of the proposed control scheme for the problem of environmental monitoring.

Guaranteed cost control of mobile sensor networks with Markov switching topologies

This paper investigates the consensus seeking problem of mobile sensor networks (MSNs) with random switching topologies. The network communication topologies are composed of a set of directed graphs (or digraph) with a spanning tree. The switching of topologies is governed by a Markov chain. The consensus seeking problem is addressed by introducing a global topology-aware linear quadratic (LQ) cost as the performance measure. By state transformation, the consensus problem is transformed to the stabilization of a Markovian jump system with guaranteed cost. A sufficient condition for global mean-square consensus is derived in the context of stochastic stability analysis of Markovian jump systems. A computational algorithm is given to synchronously calculate both the sub-optimal consensus controller gains and the sub-minimum upper bound of the cost. The effectiveness of the proposed design method is illustrated by three numerical examples.

Decentralized Control of Mobile Sensor Networks for Asymptotically Optimal Blanket Coverage Between Two Boundaries

We study a problem of blanket coverage by employing a network of self-deployed, autonomous mobile sensors or agents. The coverage problem is to drive the mobile sensor network to form a sensor lattice that completely covers a two-dimensional (2-D) region between two boundaries. In particular, the sensors form into the so-called triangular lattice pattern, and it is optimal in terms of the minimum number of sensors required for complete coverage of a bounded 2-D set. A distributed motion coordination algorithm is proposed for the mobile sensors to address the coverage problem. The algorithm is developed based on some simple consensus algorithms that only rely on local information. To illustrate the proposed algorithm, numerical simulations have been carried out for a number of scenarios.

Coupled Distributed Estimation and Control for Mobile Sensor Networks

In this paper, we introduce a theoretical framework for coupled distributed estimation and motion control of mobile sensor networks for collaborative target tracking. We use a Fisher Information theoretic metric for quality of sensed data. The mobile sensing agents seek to improve the information value of their sensed data while maintaining a safe-distance from other neighboring agents (i.e., perform information-driven flocking). We provide a formal stability analysis of continuous Kalman-Consensus filtering (KCF) algorithm on a mobile sensor network with a flocking-based mobility control model. The discrete-time counterpart of this coupled estimation and control algorithm is successfully applied to tracking of two types of targets with stochastic linear and nonlinear dynamics.

Distributed Control of Mobile Sensor Networks under RF Connectivity Constraints

This paper addresses the problem of coordinating the motion of the nodes in a mobile sensor network for area coverage applications under RF communication limitations. During network evolution, the area sensed by the network increases until it reaches optimum configuration, while information for decision making is acquired distributively among the nodes via a prespecified number of hops. Unlike previous works, radio range is not demanded to be at least twice the sensing range, imposing an extra constraint in the overall problem setup. The proposed control scheme guarantees end-to-end RF connectivity of the network, while attaining optimum area coverage. Results are further verified via simulation studies.

Tools and Techniques for Mobile Sensor Network Control

This paper compares some of the common tools and techniques that enable state-of-the-art systems to provide high-level control of mobile sensor networks. There is currently a great deal of interest in employing unmanned and autonomous vehicles in intelligence, surveillance, and reconnaissance operations. Although this paper addresses issues common to all mobile sensor networks, the applications presented are typically associated with autonomous vehicles. We focus specifically on three high-level areas: 1. mission definition languages that allow human users to compose missions defined in terms of tasks, 2. communication-addressing degradation and loss and relationship to underlying system architecture design, and 3. task allocation among the assets.

Supervisory control of mobile sensor networks: math formulation, simulation, and implementation

This paper uses a novel discrete-event controller (DEC) for the coordination of cooperating heterogeneous wireless sensor networks (WSNs) containing both unattended ground sensors (UGSs) and mobile sensor robots. The DEC sequences the most suitable tasks for each agent and assigns sensor resources according to the current perception of the environment. A matrix formulation makes this DEC particularly useful for WSN, where missions change and sensor agents may be added or may fail. WSN have peculiarities that complicate their supervisory control. Therefore, this paper introduces several new tools for DEC design and operation, including methods for generating the required supervisory matrices based on mission planning, methods for modifying the matrices in the event of failed nodes, or nodes entering the network, and a novel dynamic priority assignment weighting approach for selecting the most appropriate and useful sensors for a given mission task. The resulting DEC represents a complete dynamical description of the WSN system, which allows a fast programming of deployable WSN, a computer simulation analysis, and an efficient implementation. The DEC is actually implemented on an experimental wireless-sensor-network prototyping system. Both simulation and experimental results are presented to show the effectiveness and versatility of the developed control architecture.

Cooperative Control of Mobile Sensor Networks: Adaptive Gradient Climbing in a Distributed Environment

We present a stable control strategy for groups of vehicles to move and reconfigure cooperatively in response to a sensed, distributed environment. Each vehicle in the group serves as a mobile sensor and the vehicle network as a mobile and reconfigurable sensor array. Our control strategy decouples, in part, the cooperative management of the network formation from the network maneuvers. The underlying coordination framework uses virtual bodies and artificial potentials. We focus on gradient climbing missions in which the mobile sensor network seeks out local maxima or minima in the environmental field. The network can adapt its configuration in response to the sensed environment in order to optimize its gradient climb.