Mobile Collection Research Articles

EDTP: Energy-Density-based Trajectory Planning for Wireless Sensor Networks With Mobile Collectors

EDTP: Energy-Density-based Trajectory Planning for Wireless Sensor Networks With Mobile Collectors

An AUV-Assisted Data Gathering Scheme Based on Deep Reinforcement Learning for IoUT

The Underwater Internet of Things (IoUT) shows significant future potential in enabling a smart ocean. Underwater sensor network (UWSN) is a major form of IoUT, but it faces the problem of reliable data collection. To address these issues, this paper considers the use of the autonomous underwater vehicles (AUV) as mobile collectors to build reliable collection systems, while the value of information (VoI) is used as the primary measure of information quality. This paper first builds a realistic model to characterize the behavior of sensor nodes and the AUV together with challenging environments. Then, improved deep reinforcement learning (DRL) is used to dynamically plan the AUV’s navigation route by jointly considering the location of nodes, the data value of nodes, and the status of the AUV to maximize the data collection efficiency of the AUV. The results of the simulation show the dynamic data collection scheme is superior to the traditional path planning scheme, which only considers the node location, and greatly improves the efficiency of AUV data collection.

Holes Problem Solving in Khalimsky Topology Protocol for Wireless Sensor Networks (WSNs)

In this paper, we propose an approach solving the holes problem in Wireless Sensor Networks (WSNs) based on Khalimsky k-Clustering and data routing protocol (MDKC). The aim of this solution is to establish optimized data routing paths between isolated nodes/clusters and the Sink in noisy environment with the presence of obstacles. This approach is an improvement of a previous work deploying stationary WSN not dealing with the problem of “holes”. At first, the MDKC algorithm divides the WSN into k-hop [Formula: see text] compact dynamic clusters. For each cluster, a node is elected cluster-head in its k-neighborhood according to some criteria such as the remaining energy, the k-degree and the communication probability average. Then, some nodes are selected as Khalimsky anchors to optimize the intra-cluster data routing process. The Khalimsky anchors at the border layers ensure the inter-cluster data routing between adjacent clusters. In the next phase of MDKC, Mobile Collectors (MCs) are used for data collecting and relaying from isolated nodes/clusters to the connected Khalimsky’s nodes. The simulation results prove that MDKC minimizes the energy consumption and improves the connectivity rate between sensors and the delivery rate compared to some existing approaches.

Evaluation and modeling of a solar photovoltaic power plant: Case of the Sakal plant

This paper proposes an evaluation and modeling of the Sakal solar PV plant. In this work, we have developed and validated a model that takes into account shading effects. The studied solar power plant is mobile on a horizontal north-south axis of east-west tracking. The results showed that the model tracked the system with a correlation coefficient of 0.993. In the following, we considered the power plant fixed to evaluate the gain between the fixed and mobile collector power plants. Subsequently, we considered the plant a two-axis mobile plant. The performance of the different configurations was evaluated. The results showed that the power plant with a two-axis mobile sensor provided a gain of 40.49 % compared to the power plant configured with a fixed sensor, and a gain of 5.378 % compared to the power plant with a single-axis mobile sensor.

Task offloading optimization for AGVs with fixed routes in industrial IoT environment

In order to solve the delay requirements of computing intensive tasks in industrial Internet of things, edge computing is moving from theoretical research to practical applications. Edge servers (ESs) have been deployed in factories, and on-site auto guided vehicles (AGVs), besides doing their regular transportation tasks, can partly act as mobile collectors and distributors of computing data and tasks. Since AGVs may offload tasks to the same ES if they have overlapping path segments, resource allocation conflicts are inevitable. In this paper, we study the problem of efficient task offloading from AGVs to ESs, along their fixed trajectories. We propose a multi-AGV task offloading optimization algorithm (MATO), which first uses the weighted polling algorithm to preliminarily allocate tasks for individual AGVs based on load balancing, and then uses the Deep Q-Network (DQN) model to obtain the updated offloading strategy for the AGV group. The simulation results show that, compared with the existing methods, the proposed MATO algorithm can significantly reduce the maximum completion time of tasks and be stable under various parameter settings.

Distributed Optimization Framework for Mobile Data Collection With Energy Harvesting in Duty-Cycle WSNs

The increased dynamics and complexity of wireless sensor networks (WSNs) raises higher requirements on the flexibility and applicability of data collection algorithms. Therefore, a novel distributed optimization framework for data collection with energy harvesting in duty-cycle (DC) WSNs is developed in this article, which employs the mobile collector (called SenCar) to collect data from the selected sensors (called anchors) to circumvent the bottlenecks caused by the variations of energy distribution. In order to guarantee the optimality of mobile data collection, it is divided into two steps: Firstly, a method for calculating the optimal number of anchors is developed and then an adaptive anchor selection algorithm is proposed to determine those anchors. Secondly, the mobile data collection problem of DC WSNs is formulated as a delay optimization problem constrained by flow conversation, congestion control and energy balance. Then, we focus on designing the distributed algorithm to resolve that optimization problem, in which each sensor node only needs to communicate with its neighbors to make decisions without any global information and can locally adjust the information weight of its neighbors. Besides, the accelerated distributed algorithm with the uncoordinated step size is also proposed to improve the convergence rate. Furthermore, the explicit convergence analysis of the proposed algorithm is provided in this paper. Finally, the numerical results show that our algorithm can quickly converge to the optimal solution and has obvious superiority in adjusting link flow, storing energy, and extending network lifetime.

Collaboration-Aware Relay Selection for AUV in Internet of Underwater Network: Evolving Contextual Bandit Learning Approach

In Internet of Underwater Things, data collection is assisted by autonomous underwater vehicle (AUV) to enhance the reliable transmission. AUV acts as a mobile collector and transmits the collected data to the station via relay nodes. However, the highly mobile nature of AUV needs an adaptive and efficient relay selection scheme for achieving good capacity performance. In this article, we propose a new contextual multiarmed bandit with evolving relay set (CMAB-ERS) learning framework, which successfully addresses crucial issues, including dynamic environment conditions and evolving relay set. To deal with the evolving relay set, CMAB-ERS incorporates collaborative effects into inference as well as learning processes, the new relays will acquire prior knowledge by having experienced nodes sharing observations, reducing the learning time significantly. To overcome the uncertainty of environmental information, we exploit the contextual environment factors to assist relay reward estimation and execute time-sensitive parameter update after every transmit–receive cycle, aiming for minimizing potential loss due to the time-varying channel. Correspondingly, the collaboration-aware online contextual bandit learning (COCBL) algorithm is designed that enables AUV to switch optimal relay adaptively and promises high-capacity transmission. Further, we rigorously prove the convergence of the COCBL algorithm by considering the evolving relay set and give its upper bound on the cumulative regret. Finally, extensive simulation results elucidate the effectiveness of the proposed COCBL.

Trajectory optimization of laser-charged UAV to minimize the average age of information for wireless rechargeable sensor network

This paper considers the laser-charged Unmanned Aerial Vehicle (UAV) assisted Wireless Rechargeable Sensor Network (WRSN), where rechargeable sensors are deployed in surveillance environment to monitor information, a UAV is not only used as aerial wireless mobile collector for gathering data from sensors but also used as mobile charger to replenish energy for sensors, Laser Beam Directors (LBDs) are uniformly deployed in the monitoring environment to charge UAV by emitting laser beams. In such network, we study the average Age of Information Optimization (AoIO) problem whose objective is to minimize the average AoI of data collected from sensors such that all data of the network are transported to the base station and the remaining energy of any sensor exceeds a certain threshold. We prove that the AoIO problem is NP-hard. To solve the AoIO problem, we first study the Total Flight Time Minimizing of UAV (TFTM) problem, which aims at finding an optimal charging solution of UAV to minimize the flight time of UAV based on the order of sensors visited by UAV. Then we prove that the TFTM problem is also NP-hard. Afterwards, we propose a heuristic algorithm to solve the TFTM problem by optimizing flight path, data collection, energy power transfer and laser charging of UAV. Based on the solution for the TFTM problem, we propose an approximation algorithm to solve the AoIO problem. Finally, we conduct extensive simulation experiments to verify the effectiveness of the proposed algorithm.

Simulation of Micron and Submicron Particle Trapping by Single Droplets with Electrostatic Fields

Wet electrostatic precipitators have problems such as uneven water distribution and poor economy in applying ultra-clean particulate matter emissions from coal-fired boilers. Upgrading the droplets in wet dust removal to charged mobile collectors can effectively compensate for these shortcomings. In this paper, the effects of particle sphericity, particle size, and charge on the capture efficiency of a single droplet for capturing micron and submicron particles are qualitatively studied by simulating the process of particle capture by charged droplets in a turbulent flow field. The simulation results show that the trapping efficiency of charged droplets is positively correlated with the sphericity and the amount of charge. The particle size significantly impacts the capture efficiency, and the increase in size increases the capture efficiency, and the capture efficiency of 5.49 μm particles reaches 100%. The effect of particle movement speed on the capture efficiency needs to be considered in combination with particle size. For micron particles, the capture efficiency is close to 100% when the movement speed is 0.3 m/s and 0.5 m/s. For submicron particles, the aggregation morphology is lower at lower speeds. Simple non-spherical particles have greater capture efficiency.

Data Collection in Multihop Mobile Sink-Aided Backscatter IoT Networks

This article studies a novel wireless-powered Internet of Things (IoT) network that consists of: 1) a hybrid access point (HAP) that charges devices and also helps facilitate backscattering transmissions; 2) devices that use active radio frequency (RF) and backscattering transmissions; and 3) a mobile data collector. Our aim is to maximize the amount of data received by the HAP and data collector. The main problem is to determine the charging duration of the HAP and link activation schedule of devices. We formulate a novel mixed-integer linear program (MILP) and also propose a heuristic algorithm named reduced-set linear program approximation (RS-LPA). The results show that: 1) throughput increases with the number of backscatter transmission sets; 2) smaller amount of data is uploaded to the mobile collector when sampling cost is low; and 3) the throughput of RS-LPA is on average 10.55% lower than MILP.

An optimization-based coverage aware path planning algorithm for multiple mobile collectors in wireless sensor networks

An optimization-based coverage aware path planning algorithm for multiple mobile collectors in wireless sensor networks

Potential Function-Based Control Using Neutral Networks in Uncertain WSNs With Mobile Collectors

In wireless sensor networks (WSNs), sink node mobility has been considered an efficient way to alleviate excessive data forwarding at nodes close to the sink node. Since the mobile sinks (or collectors) move around the network collecting data from the sensor nodes, the need for multi-hop forwarding of sensor data is reduced. In WSNs with mobile collectors, efficient path planning of mobile collectors is a major challenge where many algorithms have been proposed. However, they do not consider tracking performance degradation during flight due to environmental factors such as wind, waves, and obstacles. Tracking control algorithms have been proposed to correct the paths of mobile collectors, but existing work do not consider the possibility of collisions between multiple collectors. In this paper, we propose a distributed control approach that can improve tracking performance of mobile collectors while avoiding collisions in unknown and uncertain WSN environments. The proposed algorithm uses a potential field of attraction and repulsion to control individual mobile collector input, which enables mobile collectors to adjust their trajectories with online learning in a distributed fashion. From the theoretical analysis, the upper bound of tracking error and the conditions of the system parameters to guarantee the desired tracking error set by the operator are derived. The simulation results show that the proposed method significantly outperforms existing methods in terms of tracking error, collision rate, system stability, and convergence.

Fuzzy-Based Multi-Layered Clustering and ACO-Based Multiple Mobile Sinks Path Planning for Optimal Coverage in WSNs

Wireless Sensor Network with Mobile Collectors (MCs) enable increased flexibility and convenience in data gathering for numerous large-scale applications. However, introducing MCs also brings a new set of challenges to overcome. To reduce the data delivery latency of the application, it is required to select the minimum number of Rendezvous Points (RPs) that allow most sensors to forward data in single-hop and best path must be planned for each MC to provide uniform path length and round-trip time for all MCs. In contrast to existing schemes, we propose a Fuzzy C-Means based multi-layered RP Clustering and ACO-based Route-Planning scheme (FCM-RP) which is a robust method to determine RP-positions and MC-assignments. More particularly, the existing works have given priority to either reducing network energy consumption or minimizing data gathering delay. In our paper, along with these priorities, we consider the robustness and adaptability of the algorithm; meaning that, our algorithm can modify the planned trajectories of deployed MCs in response to sensor node failures. This allows to adapt to any changes in network topology caused by node failures or external factors. Also, to maximize utilization of each RP and provide greater coverage for nodes, the ideal positioning of RPs with minimum coverage overlap with neighboring RPs is considered. Simulation analysis shows that the FCM-RP scheme defines better routes for MCs in terms of total path length and delay, and significantly outperforms the existing algorithms in terms of total energy consumption and network lifetime.

Mobile Collectors for Opportunistic Internet of Things in Smart City Environment with Wireless Power Transfer

In the context of Internet of Things (IoT) for Smart City (SC) applications, Mobile Data Collectors (MDCs) can be opportunistically exploited as wireless energy transmitters to recharge the energy-constrained IoT sensor-nodes placed within their charging vicinity or coverage area. The use of MDCs has been well studied and presents several advantages compared to the traditional methods that employ static sinks. However, data collection and transmission from the hundreds of thousands of sensors sparsely distributed across virtually every smart city has raised some new challenges. One of these concerns lies in how these sensors are being powered as majority of the IoT sensors are extremely energy-constrained owing to their smallness and mode of deployments. It is also evident that sensor-nodes closer to the sinks dissipate their energy faster than their counterparts. Moreover, battery recharging or replacement is impractical and incurs very large operational costs. Recent breakthrough in wireless power transfer (WPT) technologies allows the transfer of energy to the energy-hungry IoT sensor-nodes wirelessly. WPT finds applications in medical implants, electric vehicles, wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs), mobile phones, and so on. The present study highlights the use of mobile collectors (data mules) as wireless power transmitters for opportunistic IoT-SC operations. Specifically, mobile vehicles used for data collection are further exploited as wireless power transmitters (wireless battery chargers) to wirelessly recharge the energy-constrained IoT nodes placed within their coverage vicinity. This paper first gives a comprehensive survey of the different aspects of wireless energy transmission technologies—architecture, energy sources, IoT energy harvesting modes, WPT techniques and applications that can be exploited for SC scenarios. A comparative analysis of the WPT technologies is also highlighted to determine the most energy-efficient technique for IoT scenarios. We then propose a WPT scheme that exploits vehicular networks for opportunistic IoT-SC operations. Experiments are conducted using simulations to evaluate the performance of the proposed model and to investigate WPT efficiency of a power-hungry opportunistic IoT network for different trade-off factors.

Design, Modeling, Control, and Experiments for a Fish-Robot-Based IoT Platform to Enable Smart Ocean

With the development of robotics, the underwater robot platform has been widely used in the Internet of Underwater Things (IoUT). An underwater robot platform equipped with multiple sensors is used as a mobile collector to build a reliable information collection system for IoUT. This article presents the mechatronic design, fabrication, modeling, control, simulation, and experiments of a robot IoUT platform to enable the smart ocean. Inspired by the design of both fin-actuated swimming of fish and buoyancy-driven gliding of underwater glider, a novel multilink gliding fish robot is proposed. The multilink gliding fish robot, which is called FishBot in this article, can swim flexibly and glide energy efficiently in three dimensions. In the FishBot, the body and/or caudal fin (BCF) with three degrees of freedom and buoyancy-driven system was equipped as the main propulsion device. Besides, a pair of pectoral fins was equipped to assist in regulating the gliding attitude and enhance the FishBot maneuverability in the vertical plane. The dynamic model that consists of cruise swimming motion, pure-pitching swimming motion, and 3-D swimming motion for control is established. Moreover, a behavioral control framework is developed to achieve a variety of fish-like swimming behaviors and gliding motion. Meanwhile, we proved the stability of the linear quadratic regulation controller and the locomotion controller is provided with exponential stability. The validity of the proposed model and the designed controller is demonstrated by numerical simulations. Finally, a series of experiments involving different fish-like behaviors and gliding motion elucidates the powerful locomotion ability of the FishBot.

Gathering Big Data in Wireless Sensor Networks by Drone.

The benefits of using mobile sinks or data mules for data collection in Wireless Sensor Network (WSN) have been studied in several works. However, most of them consider only the WSN limitations and sensor nodes having no more than one data packet to transmit. This paper considers each sensor node having a relatively larger volume of data stored in its memory. That is, they have several data packets to send to sink. We also consider a drone with hovering capability, such as a quad-copter, as a mobile sink to gather this data. Hence, the mobile collector eventually has to hover to guarantee that all data will be received. Drones, however, have a limited power supply that restricts their flying time. Hence, the drone’s energy cost must also be considered to increase the amount of collected data from the WSN. This work investigates the problem of determining the best drone tour for big data gathering in a WSN. We focus on minimizing the overall drone flight time needed to collect all data from the WSN. We propose an algorithm to create a subset of sensor nodes to send data to the drone during its movement and, consequently, reduce its hovering time. The proposed algorithm guarantees that the drone will stay a minimum time inside every sensor node’s radio range. Our experimental results showed that the proposed algorithm surpasses, by up to 30%, the state-of-the-art heuristics’ performance in finding drone tours in this type of scenario.

Obstacle-Aware Connectivity Establishment in Wireless Sensor Networks

In wireless sensor networks (WSNs), the sensor nodes collect data from the target area and send it to the base station through a multi-hop connection. As a result, high energy dissipation of the sensor node may occur. Some times failure of nodes on large scale create partitions in the network. Also, the presence of obstacles is the reason for the coverage gap in networks. The introduction of mobile nodes has been measured to be a worthy approach to significantly decrease the energy dissipation of the static sensor nodes. This job is realized by considering the route through which the mobile collectors move to collect data from the sensor nodes. Another challenging job is to find the path of the mobile collector in an area with obstacles. In this work, an obstacle aware connectivity establishment (OACE) using mobile nodes in wireless sensor networks is proposed. The mobile robot collects data from each partition and carries forward to other partitions so data finally reaches to sink. To achieve this, a Delaunay triangulation based obstacle avoidance clustering has been performed. The performance of the proposed algorithm has been shown by varying the sensor nodes to a single mobile node and multiple mobile nodes. The number of partitions in the network also plays an important role in network performance. Simulation results show that the proposed method with multiple mobile collectors is more energy-efficient with less latency as compared to the single mobile collector for data collection.

Value-Based Hierarchical Information Collection for AUV-Enabled Internet of Underwater Things

The Internet of Underwater Things (IoUT) shows great potential in realizing the smart ocean. Underwater acoustic sensor networks (UWASNs) are the main existing form of IoUT but face with reliable data transmission problems. To tackle this issue, this article considers using the autonomous underwater vehicle (AUV) as a mobile collector to construct a reliable hierarchical information collection system while the Value of Information (VoI) is used as a main metric to measure the Quality of Information (QoI). We first establish a realistic model for characterizing the behaviors of AUV and sensor nodes as well as the challenging environments. Then, to construct a hierarchical architecture, we design a sink node (SN) selection scheme by jointly considering VoI conservation and energy load balancing. After that, we focus on AUV path planning with the objective of maximizing the VoI of the total network. We formulate the problem as a combinatorial optimization problem and provide an integer linear programming (ILP) model for this problem. An optimal algorithm based on the branch-and-bound (BB) method is proposed for seeking for the optimal solution, in which the lower bound and upper bound calculation strategies are specifically designed. Two near-optimal heuristic algorithms based on the concepts of the ant colony algorithm (ACA) and the genetic algorithm (GA) are also provided for further reducing the computation complexity. Finally, simulations validate the effectiveness of the proposed algorithms.

Mobile Collector-Based Cost Balancing Scheme for Uniform Data Gathering Delay and Energy Consumption in Wireless Sensor Actuator Networking Systems

The sink hole problem is a critical issue for data collection in wireless sensor networks. Recent research has indicated that the mobility of actuators in wireless sensor actuator networks (WSANs) alleviates the sink hole problem. However, mobile actuator-based data collection may result in unsatisfactory data gathering delay, which is not acceptable for delay-sensitive real time applications. Thus, planning the optimal path for mobile collector is a critical challenge in WSANs to balance between the prolongation of network lifetime and the reduction of the data gathering delay. To address this issue, we propose a mobile collector-based cost balancing algorithm to achieve uniform data gathering delay and energy consumption based on the adaptive region partitioning and dynamic polling point selection. The proposed algorithm uses multiple mobile collectors and partitions the sensing area into several regions that are monitored by the mobile collectors according to the distribution of cost in the area. It also dynamically determines moving trajectory of each mobile collector by selecting the polling points to visit considering the distance to travel and the nodes’ energy consumption. The superiority of the proposed algorithm is validated by theoretical analysis and simulation results.

Adaptive Dual-Mode Routing-Based Mobile Data Gathering Algorithm in Rechargeable Wireless Sensor Networks for Internet of Things

Great improvement recently appeared in terms of efficient service delivery in wireless sensor networks (WSNs) for Internet of things (IoT). The IoT is mainly dependent on optimal routing of energy-aware WSNs for gathering data. In addition, as the wireless charging technology develops in leaps and bounds, the performance of rechargeable wireless sensor networks (RWSNs) is greatly ameliorated. Many researches integrated wireless energy transfer into data gathering to prolong network lifetime. However, the mobile collector cannot visit all nodes under the constraints of charging efficiency and gathering delay. Thus, energy consumption differences caused by different upload distances to collectors impose a great challenge in balancing energy. In this paper, we propose an adaptive dual-mode routing-based mobile data gathering algorithm (ADRMDGA) in RWSNs for IoT. The energy replenishment capability is reasonably allocated to low-energy nodes according to our objective function. Furthermore, the innovative adaptive dual-mode routing allows nodes to choose direct or multi-hop upload modes according to their relative upload distances. The empirical study confirms that ADRMDGA has excellent energy equilibrium and effectively extends the network lifetime.