Dynamic Sensor Scheduling Research Articles

Dynamic Sensor Scheduling for Data Size Reduction in a Sensor Cloud System Based on Minimum Reconstruction Error

Dynamic Sensor Scheduling for Data Size Reduction in a Sensor Cloud System Based on Minimum Reconstruction Error

Dynamic Sensor Scheduling for Target Tracking in Wireless Sensor Networks With Cost Minimization Objective

Wireless sensor networks (WSNs) are demonstrated to be the increasingly essential systems for various Internet of Things (IoT)-based sensing applications. This article proposes a cost-aware dynamic sensor scheduling (CADSS) framework for WSNs with multiple tasks. At its core, a system cost function is designed to quantify the expenses of the WSNs due to task executions, and a task quality function is modeled to indicate the performance of the corresponding tasks. The proposed CADSS is further formulated as an optimization problem to minimize the system cost while maintaining the desired task qualities. In this way, a comprehensive task utility evaluation methodology for self-organized WSNs is constituted. Furthermore, by modeling the posterior Cramér–Rao lower bound (PCRLB) as the task quality function and a weighted sum of the communication and sensor scheduling cost as the system cost, the CADSS is instantiated into a multitarget tracking (MTT) application. It is shown that the formulated CADSS is a nonconvex optimization problem involving two coupled binary variables that, respectively, correspond to the scheduling of sensor and cluster head. We then propose a parallel convex relation approach to solve it effectively. Numerical results verify the effectiveness of the proposed CADSS by comparing it with state-of-the-art strategies.

Design of Digital Twin Sensing Strategies Via Predictive Modeling and Interpretable Machine Learning

Abstract This work develops a methodology for sensor placement and dynamic sensor scheduling decisions for digital twins. The digital twin data assimilation is posed as a classification problem, and predictive models are used to train optimal classification trees that represent the map from observed data to estimated digital twin states. In addition to providing a rapid digital twin updating capability, the resulting classification trees yield an interpretable mathematical representation that can be queried to inform sensor placement and sensor scheduling decisions. The proposed approach is demonstrated for a structural digital twin of a 12 ft wingspan unmanned aerial vehicle. Offline, training data are generated by simulating scenarios using predictive reduced-order models of the vehicle in a range of structural states. These training data can be further augmented using experimental or other historical data. In operation, the trained classifier is applied to observational data from the physical vehicle, enabling rapid adaptation of the digital twin in response to changes in structural health. Within this context, we study the performance of the optimal tree classifiers and demonstrate how they enable explainable structural assessments from sparse sensor measurements and also inform optimal sensor placement.

Adaptive Sensor Scheduling and Resource Allocation in Netted Collocated MIMO Radar System for Multi-Target Tracking

In this paper, an effective solution for adaptive sensor scheduling integrated with power and bandwidth allocation is developed for centralized multiple target tracking (MTT) in the netted collocated multiple-input multiple-output (C-MIMO) radar system. By incorporating a modified particle filter, the predicted posterior Cramer-Rao lower bound (PCRLB) is calculated. Aiming at improving the sum of target tracking accuracy of location, the predicted PCRLBs for the location of multiple targets is derived as the optimization metric. Owing to the dynamic sensor scheduling is NP-hard, a convex relaxation technique is adopted for the sensor scheduling. Taking account of the problem of power and bandwidth allocation is non-convex, a joint convex relaxation technique and cycle minimizer algorithm is adopted to convert the non-convex optimization problem into a series of convex problems. After the results of resource allocation are fed back to each node, an adaptive closed-loop system is established. Numerical results demonstrate that the location tracking accuracy can be improved efficiently by the proposed algorithm.

Event-Driven Sensor Scheduling for Mission-Critical Control Applications

In this paper, we consider a mission-critical control system, where an unstable dynamic plant is monitored by a number of distributed sensors connected to the controller over the wireless fading channels. We focus on the dynamic sensor scheduling to stabilize the unstable dynamic plant. The dynamic sensor scheduling is modeled as a non-convex drift-plus-penalty minimization problem. To improve the scheduling efficiency, the proposed scheme adapts to both the fading channel state as well as the dynamic plant state. To overcome the non-convexity in the minimization problem, we propose a novel transformation technique for the scheduling variables and the objective function (using the Lyapunov drift). Based on that, we can derive a low complexity dynamic sensor scheduling scheme and also obtain a closed-form stability analysis (despite the non-convexity) of the mission-critical control system via a randomized state-independent policy. Compared with various baselines, the proposed scheme has higher power efficiency and superior scalability performance.

Multitarget tracking in sensor networks via efficient information-theoretic sensor selection

In networks composed of moving robots or static sensing nodes, multitarget tracking is critical and fundamental for high-level applications, such as scene analysis or event detection. However, tracking multiple targets in the sensor network is challenging for two reasons: multisensor multitarget fusion itself is difficult and dynamic sensor scheduling is necessary to balance the tracking accuracy and energy consumption of the sensor network. In this article, we present a novel information-theoretic sensor selection method for multitarget tracking via the multi-Bernoulli filter. The sensor selection is based on the multi-Bernoulli filtering and a collection of subselection problems for individual target to avoid the combinatorial optimization. A subselection problem for each target is investigated under the framework of partially observed Markov decision process, and we propose to solve it by maximizing the information gain of the probability hypothesis density. Simulation results validate the effectiveness and efficiency of our method for multitarget tracking in sensor networks.

Consensus-based distributed dynamic sensor selection in decentralised sensor networks using the posterior Cramér–Rao lower bound

The paper considers the problem of dynamic sensor scheduling for non-linear tracking problems in distributed sensor/agent networks (AN/SN), where channel limitations restrict how many sensors can simultaneously participate in the estimation mechanism. Commonly referred to as sensor selection, the basic objective is to select a subset of sensors from available sensors that minimises the estimation error. The posterior Cramer–Rao lower bound (PCRLB) has recently been proposed as an effective sensor selection criteria. Nevertheless, existing PCRLB-based selectors are limited to centralised and hierarchical networks, and when extended to distributed architectures use approximate expressions. First, the non-conditional distributed PCRLB (dPCRLB) that considers observations and state variables to be random is used to derive the non-conditional sensor-selector. The dPCRLB expressions we derive are optimal. Unlike the non-conditional PCRLB, its conditional counterpart is a function of the past history of observations and is a more accurate representation of the system׳s performance. Second, the paper generalises the non-conditional dPCRLB sensor-selector to its conditional dPCRLB version. Both dPCRLB sensor-selectors use raw observations adding significant communication overhead. Third, the paper extends the conditional dPCRLB framework to quantised observations and develops the quantised version of the conditional dPCRLB sensor-selector. Numerical simulations verify the efficiency of our distributed dynamic sensor-selectors.

Dynamic Sensor Scheduling for Thermal Management in Biological Wireless Sensor Networks

Biological sensors are a very promising technology that will take healthcare to the next level. However, there are obstacles that must be overcome before the full potential of this technology can be realized. One such obstacle is that the heat generated by biological sensors implanted into a human body might damage the tissues around them. Dynamic sensor scheduling is one way to manage and evenly distribute the generated heat. In this paper, the dynamic sensor scheduling problem is formulated as a Markov decision process (MDP). Unlike previous works, the temperature increase in the tissues caused by the generated heat is incorporated into the model. The solution of the model gives an optimal policy that when executed will result in the maximum possible network lifetime under a constraint on the maximum temperature level tolerable by the patient's body. In order to obtain the optimal policy in a lesser amount of time, two specific types of states are aggregated to produce a considerably smaller MDP model equivalent to the original one. Numerical and simulation results are presented to show the validity of the model and superiority of the optimal policy produced by it when compared with two policies one of which is specifically designed for biological wireless sensor networks.

Energy-Efficient Adaptive Dynamic Sensor Scheduling for Target Monitoring in Wireless Sensor Networks

Due to uncertainties in target motion and randomness of deployed sensor nodes, the problem of imbalance of energy consumption arises from sensor scheduling. This paper presents an energy-efficient adaptive sensor scheduling for a target monitoring algorithm in a local monitoring region of wireless sensor networks. Owing to excessive scheduling of an individual node, one node with a high value generated by a decision function is preferentially selected as a tasking node to balance the local energy consumption of a dynamic clustering, and the node with the highest value is chosen as the cluster head. Others with lower ones are in reserve. In addition, an optimization problem is derived to satisfy the problem of sensor scheduling subject to the joint detection probability for tasking sensors. Particles of the target in particle filter algorithm are resampled for a higher tracking accuracy. Simulation results show this algorithm can improve the required tracking accuracy, and nodes are efficiently scheduled. Hence, there is a 41.67% savings in energy consumption.

Structured Threshold Policies for Dynamic Sensor Scheduling—A Partially Observed Markov Decision Process Approach

We consider the optimal sensor scheduling problem formulated as a partially observed Markov decision process (POMDP). Due to operational constraints, at each time instant, the scheduler can dynamically select one out of a finite number of sensors and record a noisy measurement of an underlying Markov chain. The aim is to compute the optimal measurement scheduling policy, so as to minimize a cost function comprising of estimation errors and measurement costs. The formulation results in a nonstandard POMDP that is nonlinear in the information state. We give sufficient conditions on the cost function, dynamics of the Markov chain and observation probabilities so that the optimal scheduling policy has a threshold structure with respect to a monotone likelihood ratio (MLR) ordering. As a result, the computational complexity of implementing the optimal scheduling policy is inexpensive. We then present stochastic approximation algorithms for estimating the best linear MLR order threshold policy.

Sensor measurement scheduling: an enhanced dynamic, preemptive algorithm

This paper presents an enhanced architecture for a sensor measurement scheduler as well as a dynamic sensor scheduling algorithm called the On-line, Greedy, Urgency-driven, Preemptive Scheduling Algorithm (OGUPSA). The premise is that the function of sensor management can be partitioned into the two tasks of information management, essentially an information to measurement mapping, and a sensor scheduler which takes the measurement requests along with their priorities and optimally maps them to a set of sensors. OGUPSA was developed using the three main scheduling policies of Most-Urgent-First to pick a task, EarliestCompleted-First to select a sensor, and Least-Versatile-First to resolve ties. By successive application of these policies. OGUPSA dynamically allocates, schedules, and distributes a set of measurement tasks from an information manager among a set of sensors. OGUPSA can detect the failure of a measurement task to meet a deadline and improves the dynamic load balance among all sensors while being a polynomial time algorithm. One of the key components of OGUPSA is the information in the applicable sensor table. This table is the mechanism that is used to assign requested tasks to specific sensors. Subject terms: sensor scheduling, sensor management, resource scheduling, sensors