Sensor Scheduling Research Articles

Efficient and Lightweight Model-Predictive IoT Energy Management

Multi-sensor IoT devices often rely on renewable energy and batteries to support diverse field deployments. A device’s sensors use significant energy, so careful energy management is needed to maximize its operating time while meeting application requirements. Model Predictive Control (MPC), a successful energy management technique for other application domains, requires significant computational resources usually not available in typical IoT sensing devices. We develop and evaluate a low-complexity approximation to MPC for IoT device operations powered by renewable (solar) energy, where the approximation is guided by the charging characteristics of real batteries. The complex MPC optimization problem is solved by decomposing it into a time-dependent energy allocation problem, and a task-dependent sensor scheduling problem, each of which is solvable with cubic time complexity. We utilize a novel combination of an incremental max-min fair allocation method and a recursive dynamic programming-like procedure. This low-complexity predictive optimization step is integrated with a very simple parabola-based adaptive solar prediction to provide a full system solution, termed Predictive EneRgy Management for IoT (PERMIT) , that can be implemented in lightweight IoT devices. PERMIT is evaluated through experiments on a solar-powered Raspberry Pi device with multiple sensors. We also complement our evaluations with simulations based on real device data traces. Results show that PERMIT’s low-complexity algorithm approximates the exact MPC solution very closely. PERMIT also performs significantly better than Signpost, a comparable IoT energy management solution.

Energy-efficient and location-aware IoT and WSN-based precision agricultural frameworks

Precision agriculture has emerged as a promising approach to enhance crop yield, reduce environmental impact, and optimize resource utilization through advanced sensing and automation technologies. This paper proposes an energy-efficient and location-aware framework for Internet of Things (IoT) and Wireless Sensor Networks (WSN)-based precision agriculture systems. The framework leverages low-power wireless communication protocols, adaptive sensor scheduling, and location-based clustering algorithms to minimize energy consumption and prolong the network lifetime. Key features include real-time monitoring of soil moisture, temperature, humidity, and crop health through geographically distributed sensors, with automated decision-making for irrigation, fertilization, and pest control. The proposed framework also integrates machine learning models for predictive analysis and anomaly detection, enabling early identification of potential issues that could adversely affect crop productivity. Simulation results demonstrate a significant reduction in energy consumption and communication overhead, while maintaining high accuracy in environmental parameter monitoring and resource allocation. This framework offers a scalable and robust solution for implementing sustainable precision agriculture practices, particularly in remote and resource-constrained areas

Optimal Task Scheduling and Resource Allocation for Self-Powered Sensors in Internet of Things: An Energy Efficient Approach

Optimal Task Scheduling and Resource Allocation for Self-Powered Sensors in Internet of Things: An Energy Efficient Approach

An Improved Binary Dragonfly Fault-Tolerant Sensor Scheduling Based Approach for Target Tracking in Wireless Sensor Networks

Abstract: Target tracking in wireless sensor networks (WSNs) is a critical application, often relying on node scheduling based on trajectory prediction. However, traditional approaches may lead to target loss due to prediction errors, requiring resourceintensive target recovery mechanisms and potentially resulting in data loss. To address this challenge, we propose an improved binary dragonfly fault-tolerant sensor scheduling (BD-FTSS) approach aimed at enhancing target tracking performance in WSNs. Our approach focuses on minimizing target loss probability while reducing energy consumption through a low-power scheduling mechanism. The BD-FTSS approach begins by designing a fault-tolerant domain to expand the scheduling range of candidate nodes. By considering factors such as remaining energy, sensing coverage, and overlapping coverage, we aim to activate the minimum number of sensors necessary to cover the fault-tolerant domain effectively.

Deep reinforcement learning sensor scheduling for effective monitoring of dynamical systems

Advances in technology have enabled the use of sensors with varied modalities to monitor different parts of systems, each providing diverse levels of information about the underlying system. However, resource limitations and computational power restrict the number of sensors/data that can be processed in real-time in most complex systems. These challenges necessitate the need for selecting/scheduling a subset of sensors to obtain measurements that guarantee the best monitoring objectives. This paper focuses on sensor scheduling for systems modeled by hidden Markov models. Despite the development of several sensor selection and scheduling methods, existing methods tend to be greedy and do not take into account the long-term impact of selected sensors on monitoring objectives. This paper formulates optimal sensor scheduling as a reinforcement learning problem defined over the posterior distribution of system states. Further, the paper derives a deep reinforcement learning policy for offline learning of the sensor scheduling policy, which can then be executed in real-time as new information unfolds. The proposed method applies to any monitoring objective that can be expressed in terms of the posterior distribution of the states (e.g. state estimation, information gain, etc.). The performance of the proposed method in terms of accuracy and robustness is investigated for monitoring the security of networked systems and the health monitoring of gene regulatory networks.

End-to-end multi-sensor fusion method based on deep reinforcement learning in UASNs

Underwater acoustic sensor networks (UASNs) have attracted considerable attention and are extensively employed for tracking underwater targets. However, due to the complex dynamics of underwater tracking environments, establishing accurate correlations between sensors remains elusive and challenging to precisely model. Traditional multi-sensor fusion methods for handling unknown correlations often face issues related to dependence on prior knowledge and a lack of flexibility. In response, we propose a novel end-to-end multi-sensor fusion algorithm rooted in deep reinforcement learning (DRL). To address the challenge of unknown correlations among sensors in UASNs, we formulate the multi-sensor fusion strategy as a Markov Decision Process (MDP) within the framework of reinforcement learning. The Proximal Policy Optimization (PPO) method is implemented to tackle this MDP without scalability limitations, aiming to derive an optimal sensor scheduling policy for UASNs. To enhance practical applicability, we adopt a mock data approach for algorithm training, eliminating the necessity for ground truth information on non-cooperative targets. Finally, both simulation and real-world experimental results demonstrate the superior performance of the proposed algorithm. It attains an improvement of at least 15% in tracking accuracy and exhibits a notable enhancement in stability when contrasted with traditional algorithms.

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

A Lightweight Sensor Scheduler Based on AoI Function for Remote State Estimation Over Lossy Wireless Channels

A Lightweight Sensor Scheduler Based on AoI Function for Remote State Estimation Over Lossy Wireless Channels

Ground-layer Adaptive Optics for the 2.5 m Wide-field and High-resolution Solar Telescope

The 2.5 m wide-field and high-resolution solar telescope (WeHoST) is currently under developing for solar observations. WeHoST aims to achieve high-resolution observations over a super-wide field of view (FOV) of 5′ × 5′, and a desired resolution of 0.3″. To meet the scientific requirements of WeHoST, the ground-layer adaptive optics (GLAO) with a specially designed wave front sensing system is as the primary consideration. We introduce the GLAO configuration, particularly the wave front sensing scheme. Utilizing analytic method, we simulate the performance of both classical AO and GLAO systems, optimize the wave front sensing system, and evaluate GLAO performance in terms of PSF uniformity and correction improvement across whole FOV. The results indicate that, the classical AO will achieve diffraction-limited resolution; the suggested GLAO configuration will uniformly improve the seeing across the full 5′ × 5′ FOV, reducing the FWHM across the axis FOV to less than 0.3″ (λ ≥ 705 nm, r 0 ≥ 11 cm), which is more than two times improvement. The specially designed wave front sensor schedule offers new potential for WeHoST’s GLAO, particularly the multi-FOV GLAO and the flexibility to select the detected area. These capabilities will significantly enhance the scientific output of the telescope.

Lifetime maximization of wireless sensor networks while ensuring intruder detection

Wireless sensor networks (WSN) have a wide variety of application areas and one of these areas is border crossing security. Unauthorized crossing of border areas, unauthorized arms and drug trafficking can be avoided at a lower cost and easier than conventional methods by monitoring the borders with the help of a WSN. In this study, we offer a mathematical model that guarantees the detection of possible intruders by scheduling the activities of the sensors whatever the route the intruder follows throughout the border zone or whatever the time the intruder enters to the route. To achieve the highest possible WSN management efficiency, we integrate coverage, routing, data routing, and sensor scheduling WSN design issues into the mathematical model. We first demonstrate the effectiveness of scheduling the sensors by the help of the offered mathematical model by comparing it against a random activity schedule of the sensors with respect to network lifetime and intruder detection ratio performance measures. We also develop a Lagrangean heuristic strategy to solve realistic sized instances of the proposed problem. We produce several random border zone instances with varying sizes and test the proposed solution strategy to illustrate the effectiveness of the offered solution strategy by comparing its performance against the performance of a commercial mixed-integer linear programming (MILP) solver.

A kernel-based approximate dynamic programming approach: Theory and application

This article proposes a novel kernel-based Dynamic Programming (DP) approximation method to tackle the typical curse of dimensionality of stochastic DP problems over the finite time horizon. Such a method utilizes kernel functions in combination with Support Vector Machine (SVM) regression to determine an approximate cost function for the entire state space of the underlying Markov Decision Process (MDP), by leveraging cost function computed for selected representative states. Kernel functions are used to define the so-called kernel matrix, while the parameter vector of the given kernel-based cost function approximation is computed by moving backwards in time from the terminal condition and by applying SVM regression. This way, the difficulty of selecting a proper set of features is also tackled. The proposed method is then extended to the infinite time horizon case. To show the effectiveness of the proposed approach, the resulting Recursive Residual Approximate Dynamic Programming (RR-ADP) algorithm is applied to the sensor scheduling design in multi-process remote state estimation problems.

A federated cloud-based auction mechanism for real-time scheduling of vehicle sensors in vehicle-road-cloud collaborative system

A federated cloud-based auction mechanism for real-time scheduling of vehicle sensors in vehicle-road-cloud collaborative system

Structure-Enhanced DRL for Optimal Transmission Scheduling

Remote state estimation of large-scale distributed dynamic processes plays an important role in Industry 4.0 applications. In this paper, we focus on the transmission scheduling problem of a remote estimation system. First, we derive some structural properties of the optimal sensor scheduling policy over fading channels. Then, building on these theoretical guidelines, we develop a structure-enhanced deep reinforcement learning (DRL) framework for optimal scheduling of the system to achieve the minimum overall estimation mean-square error (MSE). In particular, we propose a structure-enhanced action selection method, which tends to select actions that obey the policy structure. This explores the action space more effectively and enhances the learning efficiency of DRL agents. Furthermore, we introduce a structure-enhanced loss function to add penalties to actions that do not follow the policy structure. The new loss function guides the DRL to converge to the optimal policy structure quickly. Our numerical experiments illustrate that the proposed structure-enhanced DRL algorithms can save the training time by 50% and reduce the remote estimation MSE by 10% to 25%, when compared to benchmark DRL algorithms. In addition, we show that the derived structural properties exist in a wide range of dynamic scheduling problems that go beyond remote state estimation.

Channel Pricing and Sensor Scheduling for Distributed Estimation Based on a Stackelberg Game Framework

Channel Pricing and Sensor Scheduling for Distributed Estimation Based on a Stackelberg Game Framework

Metrics for Evaluating Interreplicate Variability of Irrigation Scheduling Sensors

Highlights Lower variability among replicates is associated with greater reliability for recommending irrigation timing. Eight metrics were presented for variability comparisons independent of sensor types and calibration accuracy. One neutron thermalization type tended to be less variable than six dielectric types in a comparison at 0.3 m depth. One granular matrix type tended to be less variable than one dielectric type in a comparison across the top 1 m of soil. Abstract. Much of the research on irrigation scheduling sensors, especially soil water sensors, assesses and refines the accuracy of sensor calibrations. However, a sensor with an accurate calibration but high variability among replicates may require a larger-than-acceptable number of replicates for informing recommendations of optimal irrigation timing. To compare the interreplicate variability of sensors across types and calibration accuracy levels, this study presented eight metrics: (1) absolute spread-to-change ratio, (2) shifted spread-to-change ratio, (3) coefficient of change variation, (4) standard deviation of relative value, (5) standard deviation of relative change, (6) standard deviation of absolute triggering date, (7) standard deviation of shifted triggering date, and (8) standard deviation of relative triggering date. These metrics enabled comparisons either by nondimensionalizing sensor measurements or by expressing interreplicate variability in terms of time. For demonstrating their usage and their particularities, the metrics were applied to two datasets that included soil water sensor types such as neutron probe (503DR), dielectric sensor (TDR-315, CS616, CS655, HydraProbe II, 5TE, TEROS 12, Drill & Drop), and granular matrix sensor (Watermark 200SS). The neutron probe in the single-depth dataset and the granular matrix sensor in the multi-depth dataset generally displayed less interreplicate variability than other evaluated sensor types over multiple drying cycles. Future research is suggested to calculate and improve the eight metrics for identifying combinations of sensor types, deployment methods, and data interpretation techniques that minimize interreplicate variability and maximize irrigation scheduling precision. Keywords: Assessment, Comparison, Index, Nondimensionalization, Precision, Reliability, Soil water, Standardization, Uncertainty, Variation.

Design of routing protocol for enhancing quality of service in wireless ad hoc and sensor netw ork: LEQA

<p>Wireless sensor networks (WSNs) are now adopting mobile sensors due to their increased popularity in research and industry. To enhance WSNs' performance, mobility can be utilized to gather data. But, if the collector's route is fixed and movement is not manageable, current quality of service (QoS) strategies and protocols are ineffective in achieving timely data delivery while maintaining energy efficiency. In the real world, WSN networks use both actuator - actuator and sensor - actuator coordination. To conserve energy in communicati on tasks with heavy traffic and high volume, sensors/actuators can be relocated to desired locations. This study introduces a routing protocol that optimizes delivery latency and energy conservation in WSNs. The proposed latency, energy, and quality of ser vice aware (LEQA) protocol uses a cooperative approach to track the sink and coordinate communication between sensors and actuators. Each sensor schedules its time division multiple access (TDMA) to improve QoS metrics such as low energy consumption, low l atency, or packet loss. It also addresses sensor - actuator coordination and proposes a data communication protocol for efficient and fast communication with actuator nodes. This reduces energy consumption and minimizes latency.</p>

Double Threshold Structure of Sensor Scheduling Policy Over a Finite-State Markov Channel.

In this article, we consider the optimal sensor scheduling for remote state estimation in cyber-physical systems (CPSs). Different from the existing works concerning the time-invariant channel state in the wireless communication network, our work considers the time-varying channel state modeled by a finite-state Markov channel (FSMC). We focus on the problem of how to schedule the transmission of the sensor to minimize the estimation error at the remote side with less communication cost. Using the framework of the Markov decision process (MDP), the optimal scheduling policy is shown to be deterministic stationary (DS). We further derive its double threshold structure with respect to remote estimation errors and channel states. Moreover, a necessary and sufficient condition guaranteeing the mean-square stability of the remote estimator is given based on the structured scheduling policy. Numerical simulations are provided to verify the theoretical results.

Sensor Scheduling for Remote State Estimation with Limited Communication Resources: A Time- and Event-Triggered Hybrid Approach.

This paper proposes a time- and event-triggered hybrid scheduling for remote state estimation with limited communication resources. A smart sensor observes a physical process and decides whether to send the local state estimate to a remote estimator via a wireless communication channel; the estimator computes the state estimate of the process according to the received data packets and the known scheduling mechanism. Based on the existing optimal time-triggered scheduling, we employ a stochastic event trigger to save precious communication chances and further improve the estimation performance. The minimum mean-squared error (MMSE) state estimate is derived since the Gaussian property is preserved. The estimation performance upper bound and communication rate are analyzed. The main results are illustrated by numerical examples.

Nonlinear event-based state estimation using particle filter under packet loss

In this research paper, we investigate the problem of remote state estimation for nonlinear discrete systems. Specifically, we focus on scenarios where event-triggered sensor schedules are utilized and where packet drops occur between the sensor and the estimator. In the sensor scheduler, the SOD mechanism is proposed to decrease the amount of data transmitted from the sensor to a remote estimator and the phenomena of packet drops modeled with random variables obeying the Bernoulli distribution. As a consequence of packet drops, the assumption of Gaussianity no longer holds at the estimator side. By fully considering the non-linearity and non-Gaussianity of the dynamic system, this paper develops an event-trigger particle filter algorithm to relieve the communication burden and achieve an appropriate estimation accuracy. First, we derive an explicit expression for the likelihood function when an event trigger occurs and the possible occurrence of packet dropout is taken into consideration. Then, using a special form of sequential Monte–Carlo algorithm, the posterior distribution is approximated and the corresponding minimum mean-squared error is derived. By contrasting the error covariance matrix with the posterior Cramér–Rao lower bound, the estimator’s performance is assessed. An illustrative numerical example shows the effectiveness of the proposed design.

An end-to-end sensor scheduling method based on D3QN for underwater passive tracking in UWSNs

The underwater wireless sensor networks (UWSNs) receive extensive concern and become common tools for underwater target passive tracking. To address the energy constraints of battery-powered sensors in UWSNs, efficient sensor scheduling methods that balance tracking accuracy and energy consumption are essential. Considering the common sensor scheduling methods facing with the problems of strong environment dependence and lack flexibility, an end-to-end sensor scheduling algorithm based on the deep reinforcement learning is proposed. In particular, we formulate the sensors’ scheduling strategy in UWSNs as Markov decision process (MDP) within the context of reinforcement learning. Moreover, a Dueling double deep Q network (D3QN) is introduced to solve the MDP in a scalable and model free manner for a suitable sensor scheduling policy of UWSNs and the prioritized experience replaybuffer (PER) is utilized to improve the performance of D3QN. In addition, to ensure practical applicability, a mock data method is introduced to train the algorithm without relying on accurate trajectory information of Non-cooperative target. Thus, an end-to-end sensor scheduling method can be built. Experimental results demonstrate the effectiveness and superiority of the proposed algorithm. In large-scale UWSNs for underwater passive tracking, the proposed method achieves a minimum of 20% improvement in tracking accuracy while reducing the system’s energy consumption by 10% compared to the traditional method.