Scheduling In Wireless Sensor Networks Research Articles

Improved Soft-k-Means Clustering Charging Based on Node Collaborative Scheduling in Wireless Sensor Networks

Improved Soft-k-Means Clustering Charging Based on Node Collaborative Scheduling in Wireless Sensor Networks

Duty Cycle Scheduling in Wireless Sensor Networks Using an Exploratory Strategy-Directed MADDPG Algorithm

This paper presents an in-depth study of the application of Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithms with an exploratory strategy for duty cycle scheduling (DCS) in the wireless sensor networks (WSNs). The focus is on optimizing the performance of sensor nodes in terms of energy efficiency and event detection rates under varying environmental conditions. Through a series of simulations, we investigate the impact of key parameters such as the sensor specificity constant α and the Poisson rate of events on the learning and operational efficacy of sensor nodes. Our results demonstrate that the MADDPG algorithm with an exploratory strategy outperforms traditional reinforcement learning algorithms, particularly in environments characterized by high event rates and the need for precise energy management. The exploratory strategy enables a more effective balance between exploration and exploitation, leading to improved policy learning and adaptation in dynamic and uncertain environments. Furthermore, we explore the sensitivity of different algorithms to the tuning of the sensor specificity constant α, revealing that lower values generally yield better performance by reducing energy consumption without significantly compromising event detection. The study also examines the algorithms' robustness against the variability introduced by different event Poisson rates, emphasizing the importance of algorithm selection and parameter tuning in practical WSN applications. The insights gained from this research provide valuable guidelines for the deployment of sensor networks in real-world scenarios, where the trade-off between energy consumption and event detection is critical. Our findings suggest that the integration of exploratory strategies in MADDPG algorithms can significantly enhance the performance and reliability of sensor nodes in WSNs.

Population-Invariant MADRL for AoI-Aware UAV Trajectory Design and Communication Scheduling in Wireless Sensor Networks

Population-Invariant MADRL for AoI-Aware UAV Trajectory Design and Communication Scheduling in Wireless Sensor Networks

Distributed homology-based sensor selection and scheduling in wireless sensor networks

Distributed homology-based sensor selection and scheduling in wireless sensor networks

ECKN: An Integrated Approach for Position Estimation, Packet Routing, and Sleep Scheduling in Wireless Sensor Networks.

Network lifetime and localization are critical design factors for a number of wireless sensor network (WSN) applications. These networks may be randomly deployed and left unattended for prolonged periods of time. This means that node localization is performed after network deployment, and there is a need to develop mechanisms to extend the network lifetime since sensor nodes are usually constrained battery-powered devices, and replacing them can be costly or sometimes impossible, e.g., in hostile environments. To this end, this work proposes the energy-aware connected k-neighborhood (ECKN): a joint position estimation, packet routing, and sleep scheduling mechanism. To the best of our knowledge, there is a lack of such integrated solutions to WSNs. The proposed localization algorithm performs trilateration using the positions of a mobile sink and already-localized neighbor nodes in order to estimate the positions of sensor nodes. A routing protocol is also introduced, and it is based on the well-known greedy geographic forwarding (GGF). Similarly to GGF, the proposed protocol takes into consideration the positions of neighbors to decide the best forwarding node. However, it also considers node residual energy in order to guarantee the forwarding node will deliver the packet. A sleep scheduler is also introduced in order to extend the network lifetime. It is based on the connected k-neighborhood (CKN), which aids in the decision of which nodes switch to sleep mode while keeping the network connected. An extensive set of performance evaluation experiments was conducted and results show that ECKN not only extends the network lifetime and localizes nodes, but it does so while sustaining the acceptable packet delivery ratio and reducing network overhead.

Multilayer Joint Optimization of Packet Size and Adaptive Transmission Scheduling of Wireless Sensor Networks for Mechanical Vibration Monitoring

Limitations pertaining to the storage capacity and bandwidth of wireless sensor networks (WSNs) for mechanical vibration monitoring (MVM) pose difficulties in the realization of reliable and energy-efficient communication of big data. To prolong the lifetime of battery-limited WSNs, this paper proposes a multi-layer joint optimization of packet size and adaptive transmission scheduling (ATS) to jointly optimize the energy consumption of acquisition nodes from the storage and transmission layers. First, owing to the frequent storage and transmission of massive mechanical vibration data in WSNs, the packet size significantly affects the energy consumption during storage and transmission. The proposed packet size optimization based on the lowest energy consumption (PSO-LEC) model optimizes the energy consumption of acquisition nodes from three aspects: writing, reading, and transmission of data. Second, when transmitting massive vibration data frequently, the direct beacon frame conflict and indirect beacon frame conflict markedly induce data conflict and delay problems. Therefore, adaptive transmission scheduling based on the time division multiple access (TDMA/ATS) method is proposed to optimize the errors in data collision and transmission delay. Thus, the packet size and ATS are considered jointly to improve the network lifetime for MVM. Comprehensive experimental results indicate that the transmission reliability and energy-efficiency of the proposed scheme are significantly improved, and the network life is effectively prolonged for MVM.

On optimally solving sub‐tree scheduling for wireless sensor networks with partial coverage: A branch‐and‐cut algorithm

AbstractGiven a wireless sensor network, we consider the problem to minimize its total energy consumption over consecutive time slots with respect to communication activities. Nonempty and disjoint subsets of nodes are required to be active and connected under a tree topology configuration in the different time slots, and each network node must be active in a unique time slot. Moreover, the power required by the same pair of network nodes to communicate on the associated direct channel may vary in the different time slots. The problem has been recently introduced in the literature under the name Sub‐Tree Scheduling for Wireless Sensor Networks with Partial Coverage. We focus on the exact solution of the problem. We present a branch‐and‐cut (BC) algorithm based on a novel integer linear programming formulation which allows avoiding the introduction of symmetries in the solution space. In particular, the algorithm relies on an efficient and nontypical separation algorithm for known valid inequalities, and on an easy‐to‐implement primal bound heuristic. The effectiveness of the BC algorithm is empirically shown through an extensive experimental analysis involving 300 newly generated benchmark instances with up to 200 network nodes and 8 time slots. Additionally, the experimental results show that the BC algorithm represents a valid computational tool to benchmark the performance of heuristics addressing the problem, and can be used in practice, as an heuristic solver, to tackle problem instances that are not too large.

AIMD Rule-Based Duty Cycle Scheduling in Wireless Sensor Networks Using Quartile-Directed Adaptive Genetic Algorithm

Wireless sensor networks (WSNs) are attractive, in large part because they do not require wired infrastructure. However, this very characteristic renders them energy-constrained. Duty cycle scheduling (DCS) is thought to contribute to the energy efficiency of sensing. We develop a novel paradigm for modeling WSNs and a DCS strategy based on the additive increase/multiplicative decrease (AIMD) rule. The scheduling problem is framed as an optimization problem with the optimization objectives of lowering energy consumption and enhancing detection ability. The classic GA converges slowly in late evolution phases for using fixed crossover and mutation probability. An adaptable genetic algorithm (AGA) eliminates the above concern by allowing modifying crossover and mutation probability as per fitness values, but, unfortunately, it often fails to select the best individuals. We suggest a quartile-directed adaptive genetic algorithm (Q-AGA) in which individuals with fitness above the first quantile of the last generation were selected. The interquartile range (IQR) is utilized to weight crossover and mutation probability. If IQR is larger, the fitness distribution is more concentrated, and then, we can diminish the crossover and mutation probability, allowing favorable traits to be retained to the greatest extent feasible. Simulation results show that Q-AGA has a faster convergence speed and a stronger global search ability. Furthermore, we change the hypothesis of the system model by considering that the two objectives can be weighted, thereby broadening the application scenarios.

Optimum Partition for Wireless Charging Scheduling in Wireless Sensor Networks With Applications

Previous research on wireless charging did not discuss the problem of using optimal allocation to improve the network life cycle. The authors designed and analyzed a variety of network area cutting methods: rectangular cutting, square cutting, concentric circle cutting, sector cutting, and mixed cutting. Through simulation experiments, the difference between the survival time and the number of received packets is compared. The experimental results show that mixed cutting can calculate the expected energy consumption according to the energy consumption rate, and then allocate chargers according to the expected energy consumption, making the energy consumption burden of each charger more equitable. Compared with other partition methods, the load capacity distribution of each charger is more uniform. In terms of survival time and receiving message packets, the network can have a longer survival time, receive more message packets, and use the power consumption of each block more evenly and effectively.

Energy-Efficient Compressive Sensing Based Data Gathering and Scheduling in Wireless Sensor Networks

Energy-Efficient Compressive Sensing Based Data Gathering and Scheduling in Wireless Sensor Networks

Mixed Linear Programming for Charging Vehicle Scheduling in Large-Scale Rechargeable WSNs

Because wireless sensor networks (WSNs) have low-constrained batteries, optimizing the network lifetime is a primary challenge. Rechargeable batteries are a solution to prolong the lifetime of a sensor node instead of restricting their functionalities to save energy. Wireless energy transmitters have the added benefit of providing a charger for the batteries of the sensor nodes in the WSN. However, scheduling one or more charging vehicles efficiently to recharge multiple sensor nodes is challenging. In this context, this paper provides a solution to recharge the sensor nodes using charging vehicle scheduling in WSNs through a mixed linear programming approach. Initially, we identify a heuristic value of each sensor node based on their residual energy, distance from a charging vehicle, available data packets, and other metrics. Further, a set of nodes is recharged by identifying the best charging vehicle to prolong their lifetimes, as well as the lifetime of the network as a whole. We simulated the proposed approach using a Python simulator, tested using different performance metrics, and compared using the recently published works. We notice the superior performance of the proposed work under various metrics in time and query-driven WSNs.

A Convolution Location Method for Multi-Node Scheduling in Wireless Sensor Networks

The localization of continuous objects and the scheduling of resources are challenging issues in wireless sensor networks (WSNs). Due to the irregular shape of the continuous target area and the sensor deployment in WSNs, the sensor data are always discrete and sparse, and most network resources are also limited by the node energy. To achieve faster detection and tracking of continuous objects, we propose a convolution-based continuous object localization algorithm (named CCOL). Moreover, we implement the idea of greedy and dynamic programming to design an energy-saving and efficient strategy model (named MSSM) to respond to emergencies caused by multiple continuous targets in most specific WSNs. The simulation experiments demonstrate that CCOL is superior to other localization algorithms in terms of time complexity and execution performance. Furthermore, the feasibility of the multinode scheduling strategy is verified by setting different mobile nodes to respond to the target area in certain green WSNs.

Joint UAV Trajectory and Data Demand for Scheduling in Wireless Sensor Networks

Energy reduction for sireless sensor networks (WSNs) is considered in many works. Sensor scheduling for data collection where the sensors in a WSN are set to wake-up and sleeping mode is a promising candidate. Unmanned aerial vehicle (UAV) utilizing the mobility is investigated to support the data collection for WSNs. This paper proposes a novel system and timing model where a UAV flies to hover at sensors and collect the sensed data. Therefore, a design problem for operationtime minimization is established as a combination problem. To overcome the challenge, we propose a heuristic search method to find the trajectory of UAV. The numerical results demonstrate that the proposed method outperforms the random one.

Agent driven resource scheduling in wireless sensor networks: fuzzy approach

Agent driven resource scheduling in wireless sensor networks: fuzzy approach

Partly Centralized Partly Distributed Energy Efficient Sleep/Wake Scheduling in Wireless Sensor Networks for Applications Requiring Continuous Sensing

A wireless sensor network requires judicious utilization of its limited energy resources. As a result, the energy efficient algorithms have become a pre-requisite for healthy and prolonged functioning of these networks. Consequently, the present work minimizes the energy expenditure, maximizes the node utilization, incurs lesser state switching, and prolongs the network lifetime by proposing a partly centralized and partly distributed sleep wake scheduling for the overlapped dominating sets. The concepts of such a hybrid scheduling and overlapped dominating sets have not been explored to their full potential for continuous sensing. Therefore, the research work presented in this paper shall be a stepping stone to further explore the suggested concept for the purpose of reducing energy consumption in wireless sensor networks. It is envisioned that the proposed approach shall be beneficial in battlefield monitoring, security surveillance, environmental monitoring etc., which necessitate long-term continuous/non-periodic coverage of the target region. The proposed sleep wake scheduling algorithm is hybrid in nature and possesses the advantages of both distributed and centralized approaches. The superiority and significance of the results have been established by a comparative analysis with other existing techniques mentioned in the literature.

Network Performance Metrics for Energy Efficient Scheduling in Wireless Sensor Networks (WSNs)

In Wireless Sensor Networks, sensor nodes are deployed to ensure continuous monitoring of the environment which requires high energy utilization during the data transmission. To address the challenge of high energy consumption through frequent independent data transmission, the IEEE 802.11b provides a backoff window that reduces collisions and energy losses. In the case of Internet of Things (IoTs), billions of devices communicate with each other simultaneously. Therefore, adapting the contention/backoff window size to data traffic to reduce congestion has been one such approach in WSN. In recent years, the IEEE 802.11b MAC protocol is used in most ubiquitous technology adopted for devices communicating in the IoT environment. In this paper, we perform a thorough evaluation of the IEEE 802.11b standard taking into consideration the channel characteristics for IoT devices. Our evaluation is aimed at determining the optimum parameters suitable for network optimization in IoT systems utilizing the IEEE 802.11b protocol. Performance analysis is made on the sensitivity of the IEEE 802.11b protocol with respect to the packet size, packet delivery ratio (PDR), end‐to‐end delay, and energy consumption. Our studies have shown that for optimal performance, IoT devices using IEEE 802.11b channel require data packet of size 64 bytes, a data rate of 11Mbps, and an interpacket generation interval of 4 seconds. The sensitivity analysis of the optimal parameters was simulated using NS3. We observed PDR values ranging between 27% and 31%, an average end‐to‐end delay ranging within 10‐15 ms while the energy remaining was between 5.59 and 5.63Joules. The results clearly indicate that scheduling the rate of packet generation and transmission will improve the network performance for IoT devices while maintaining data reliability.

Utilizing priority-based packet scheduling based on preemptive priority scheduling in wireless sensor networks

In this paper, each node has three levels of priority queues. Real-time packets are placed into the highest-priority queue and can preempt data packets in other queues. Non-real-time packets are placed into two other queues based on a certain threshold of their estimated processing time. Leaf nodes have two queues for real-time and non-real-time data packets since they do not receive data from other nodes and thus, reduce end-to-end delay. The priority packet scheduling scheme outperforms conventional schemes in terms of average data waiting time and end-to-end delay and also it reduces sensor energy consumption.

CDARGA: Cluster-Based Data Aggregation with Genetic Routing Algorithm in Wireless Sensor Networks

In the wireless sensor networks (WSNs), the upholding the energy and routing formation at every sensor node is the major issues. The distance from base station and internal node mainly has imbalanced in the energy consumption during transformation of the data. To reduce the energy upholding and the data aggregation routing issues in Centralized Clustering-Task Scheduling for wireless sensor networks (WSNs), this paper focuses on a Cluster-Based Data Aggregation Routing with Genetic search Algorithm (CDARGA) , which reduces the energy consumption in a hyper round model. The proposed data aggregation routing protocol using the Genetic Algorithm (GA) estimates the fitness function using the three key parameters distance, energy, and Hyper round policy. The proposed methods were compared with RP-MAC and the experimental result shows that the proposed protocol is superior to RP-MAC protocol and the proposed algorithm improves the network lifetime which can used in real time application.

Energy Efficient Target Tracking Method for Multi-Sensory scheduling in Wireless Sensor Networks

Data collection utilizing wireless sensors networks (WSNs) has been utilized for surveillance, monitoring environment, animal etc. Target tracking of maneuvering objects is an essential need of modern life. Nonetheless, because of diverse nature of sensor and complex environment, sensors measurement errors need to be minimized considering diverse motion states in process of tracking (sensing) operation. Enhancing network lifetime (i.e., reducing energy dissipation of sensor nodes) and improving tracking quality are major concern of target tracking using WSN. Form improving network energy efficiency, multi-sensory target tracking method has been modelled using Kalman Filter (KF) by existing target tracking method. The KF based model are affected due to presence of noise or missing data. For overcoming research issues this paper present an H-infinity filter (HF) to evaluate fusion for maneuvering target tracking in WSN. Further, to minimize the estimation errors and reduces/controlling the effects of outliers fuzzy H-infinity (FHF) filter for target tracking WSN is presented. Experiment outcome shows proposed HF and FHF fusion model attain better performance than existing KF based method for clustered based WSN in terms of positional and velocity root mean square error and energy dissipation.

Energy Efficient Opportunistic Routing with Sleep Scheduling in Wireless Sensor Networks

The network life of wireless sensor networks (WSNs) relies on the limited energy of non-rechargeable batteries used at the sensor node. Hence, maximum energy saving is essential in the research area while designing a routing algorithm for the WSNs. An energy-saving opportunistic routing (ENS_OR) uses an opportunistic routing concept to improve network performance while relaying data. In this paper, the ENS_OR is further revised with a sleep scheduling algorithm to reduce energy dissipation in one dimensional topology. The proposed sleep scheduling algorithm is designed to enhance network performance by minimizing energy dissipation due to the idle listening of nodes. Sleep interval is adaptive, and it is made proportional to the residual energy of nodes as well as the flow rate of the network. The results of the proposed algorithm are analyzed and compared with ENS_OR without sleep mode and other routing protocols used in WSNs. The results prove that ENS_OR with sleep mode is beneficial to conserve energy for a prolonged lifetime.