Smartphone-based data collection from wireless sensor networks in an urban environment

To satisfy the increasing demand of wireless sensor network (WSN) distribution in the different monitoring area and enlarge the coverage of the WSNs in various scenes of application, in this paper, we constructed a new type of mobile wireless data sinking platform for collecting the data of WSN based on the technology of unmanned aerial vehicle (UAV). First, the authors studied a wireless environmental monitoring system containing the UAV platform and WSN is constructed and both test on wireless communication capacity and data collecting are carried out. The communication capacity test show that when the RF modules are operated with transmission power greater than 1 dBm and communication distance less than 100 m, the UAV wireless sink node can maintain a high quality communication data link which has a lower PLR (less than the 5 %) and better signal level (RSSI greater than −90 dBm) with transmission data rate of 250 kbps, using this RF transceiver a simple communication & data collection protocol between UAV and ground WSN sink node was proposed. The whole work of this paper should provide some experience for building a UAV cooperative WSN system in the future.

Due to the energy limitation in Wireless Sensor Networks (WSNs), most researches related to data collection in WSNs focus on how to collect the maximum amount of data from the network with minimizing the energy consumption as much as possible. Many types of research that are related to data collection are proposed to overcome this issue by using mobility with path constrained as Maximum Amount Shortest Path routing Protocol (MASP) and zone-based algorithms. Recently, Zone-based Energy-Aware Data Collection Protocol (ZEAL) and Enhanced ZEAL have been presented to reduce energy consumption and provide an acceptable data delivery rate. However, the time spent on data collection operations should be taken into account, especially concerning real-time systems, as time is the most critical factor for these systems' performance. In this paper, a routing protocol is proposed to improve the time needed for the data collection process considering less energy consumption. The presented protocol uses a novel path with a communication time-slot assignment algorithm to reduce the count of cycles that are needed for the data collection process with reduction of 50% of the number of cycles needed for other protocols. Therefore, the time and energy needed for data collection are reduced by approximately 25%and 6% respectively, which prolongs the network lifetime. The proposed protocol is called Energy-Time Aware Data Collection Protocol (ETCL).

Vehicular sensor networks (VSNs) have emerged as a new technology, which combines wireless communication offered by vehicular ad hoc NETworks with sensing devices installed in vehicles or in roadsides. VSN creates a huge opportunity to extend the roadside sensor infrastructure of existing traffic control systems. The main issues in VSNs are inherent in the data collection and its dissemination in such a large-scale network, characterized by frequent topology changes and network partitioning. This paper proposes a new multi-hop data collection and dissemination protocol based on data replication approach. The aim is an efficient collection and dissemination of data from a defined geographic area. Differently from other data collection protocols for VSNs, which have for purpose to collect data for traffic control applications or monitoring applications, our proposed protocol called Collaborative Data Collection Protocol (CDCP for VSNs) focuses on collecting data to be used in non-delay tolerant applications, so CDCP aims to collect the requested data within an optimized delay, but also, focuses in making this data accessible for other potential requests. Simulation results show the effectiveness of our proposed protocol.

Wireless sensor networks (WSNs) are used to collect large amounts of data over a wide area. However, the sensor nodes used in WSNs have a short lifespan because they are generally battery-operated. Research is actively underway to increase the lifespan of WSNs via techniques such as energy harvesting and wireless power transfer (WPT). This study proposes a scheme for clustering and adjusting the WSN data collection rate to alleviate the hotspot problem and improve network connectivity. The proposed scheme is targeted at WSNs in which the sensor nodes harvest solar energy. In addition, an unmanned aerial vehicle (UAV) traverses along a given path and delivers energy to sensor nodes using WPT, while collecting data as a sink node. In this scheme, the number of cluster heads is determined by considering the number of nodes at each hop distance and the maximum amount of data that can be transferred with the available energy. Further, sensor nodes limit the amount of data they collect to reduce the relay load on intermediate nodes. To achieve this, nodes consider the amount of data transferred by parent nodes, especially nodes in the hotspot, and the number of child nodes. The sink node traverses along a given path, and collects the data accumulated in the cluster head while supplying its remaining energy to the cluster head. The amount of energy it transfers is determined by considering the number of cluster heads. Consequently, nodes in the hotspot are prevented from becoming blacked out from a lack of energy, and the energy harvesting efficiency is increased. Simulation results indicate that the proposed scheme reduces the number of blackout nodes in the hotspot areas near cluster heads. Furthermore, the data collection and monitoring performance increases with the increase in network connectivity.

In wireless sensor networks, the main work of nodes is for data collection and transmission. CTP (Collected Tree Protocol) had been widely applied, and proved to be reliable, robust, efficient data collection protocol. Its independent with hardware and the data delivery rate is relatively high, but CTP also has some shortcomings: the protocol only relied on the ETX value of next hop node selection, there need other factors to consider. In the original CTP protocol, the energy consumption of a node with a better ETX value was too fast. It would cause the whole network time decreased quickly. Then, the node with better ETX value may have low package rate, and would affect the delivery rate of the whole networks. This paper was aimed at this two aspects of CTP, based on the original CTP, the probability of selection mechanism was introduced, proposed ACTP, lead the node load equilibrium and improved the delivery rate. Through the experimental data of TelosB nodes in real analysis, we had improved the whole network time and increased the number of successful packets delivery.

The performance of data collection in Wireless Sensor Networks (WSNs) can be measured by network capacity. However, few existing works dedicatedly consider the Continuous Data Collection (CDC) capacity for WSNs under the protocol interference model. In this paper, we propose a multipath scheduling algorithm for SDC in single-radio multichannel WSNs and derive its network capacity which is a tighter lower bound compared with the previously best result [CHECK END OF SENTENCE]. We also propose a novel CDC method for dual-radio multichannel WSNs. It significantly speeds up the data collection process, and achieves a capacity of (nW/12M⌈(3.63ρ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> +c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> ρ+c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> )/H⌉) when Δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> ≤ 12 or (nW/MΔ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> ⌈(3.63ρ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> +c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> ρ+c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> )/H⌉) when Δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> >;12, where n is the number of the sensors, M is a constant value and usually M ≪ n, Δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> is the maximum number of the leaf nodes having a same parent in the data collection tree, W is the channel bandwidth, H is the number of available orthogonal channels, \rho is the ratio of the interference radius over the transmission radius, c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> = (8π/√(3)) + π + 2, and c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> = (8π/√(3)) + 2π + 6. Extensive simulation results indicate that the proposed algorithms improve network capacity significantly compared with existing works.

Data collection is one of the basic tasks of WSN, and data collection protocol occupies a pivotal position in WSN. Data collection protocol in TinyOS 2.0 is implemented in the form of Collection Tree Protocol (CTP). However, CTP have proved to be extremely efficient [1], in practical applications, CTP also have some disadvantages. The paper presents an improved data collection protocol ICTP based on the analysis of CTP. The protocol introduces the concept of load balance into CTP. Finally, with the simulation results, we have proved that our improvement improves the efficiency and performance of the data collection task in WSN. There is something noteworthy that ICPT doesn't have additional energy as a result of not selecting the optimal path.

Wireless sensor networks (WSNs) are an important means of collecting data in a variety of situations, such as the monitoring of large or hazardous areas. The retrieval of WSN data can yield better results through the use of unmanned aerial vehicles (UAVs), for example, concerning the increase in the amount of data collected and the decrease in the time between the collection and use of the data. In particular, disaster areas may be left without communication resources and with high residual risk to humans, at which point a WSN can be quickly launched by air to collect relevant data until other measures can be established. The set of rules of each problem’s component (e.g., number of UAVs, UAVs dislocation control, sensors, communication) is considered the approaches to solve the problem. In this meaning, some studies present approaches for the use of UAVs for the collection of WSN data, focusing primarily on optimizing the path to be covered by a single UAV and relying on long-range communication that is always available; these studies do not explore the possibility of using several UAVs or the limitations on the range of communication. This work describes DADCA, a distributed scalable approach capable of coordinating groups of UAVs in WSN data collection with restricted communication range and without the use of optimization techniques. The results reveal that the amount of data collected by DADCA is similar or superior to path optimization approaches by up to 1%. In our proposed approach, the delay in receiving sensor messages is up to 46% shorter than in other approaches, and the required processing onboard UAVs can reach less than 75% of those using optimization-based algorithms. The results indicate that the DADCA can match and even surpass other presented approaches, since the path optimization is not a focus, while also incorporating the advantages of a distributed approach.

Data collection is a fundamental task in wireless sensor networks. In many applications of wireless sensor networks, approximate data collection is a wise choice due to the constraints in communication bandwidth and energy budget. In this paper, we focus on efficient approximate data collection with prespecified error bounds in wireless sensor networks. The key idea of our data collection approach ADC (Approximate Data Collection) is to divide a sensor network into clusters, discover local data correlations on each cluster head, and perform global approximate data collection on the sink node according to model parameters uploaded by cluster heads. Specifically, we propose a local estimation model to approximate the readings of sensor nodes in subsets, and prove rated error-bounds of data collection using this model. In the process of model-based data collection, we formulate the problem of selecting the minimum subset of sensor nodes into a minimum dominating set problem which is known to be NP-hard, and propose a greedy heuristic algorithm to find an approximate solution. We further propose a monitoring algorithm to adaptively adjust the composition of node subsets according to changes of sensor readings. Our trace-driven simulations demonstrate that ADC remarkably reduces communication cost of data collection with guaranteed error bounds.

Wireless Sensor Networks (WSNs) connected to the Internet of Things (IoT) are increasingly employed in commercial and industrial applications to accomplish various tasks at a low cost. WSNs are essential for gathering diverse types of data within physical environments. A key design objective for WSNs is to balance energy consumption and increase the network’s operating lifetime. Recent studies have shown that mobile base stations (BSs) can significantly extend the lifetime of such networks, especially when their location is optimized using specific criteria. In this study, we propose an algorithm for selecting the optimal BS location in a large network. The algorithm computes a distance metric between sensor nodes (SNs) and potential BS locations on a virtual grid within the WSN. The selection process is repeated periodically to account for dead SNs, allowing the BS to relocate to a new optimal position based on the remaining active nodes after each iteration. Additionally, the inclusion of a relay node (RN) in large networks is explored to improve scalability. The impact of path loss within WSNs is also discussed. The proposed algorithms are applied to the well-known Stable Election Protocol (SEP). Simulation results demonstrate that, compared to other algorithms in the literature, the proposed approaches significantly enhance the lifetime of WSNs.

A UAV-assisted CH election framework for secure data collection in wireless sensor networks

In this paper, we propose a new data gathering mechanism for large scale wireless sensor networks by introducing mobility into the network. A mobile data collector, or for convenience called M-collector in this paper, could be a mobile robot or a vehicle equipped with a powerful transceiver and battery, works like a mobile base station and gathers data while moving through the field. An M-collector starts the data gathering tour periodically from the static data sink, polls each sensor while traversing the transmission range of the sensor, then collects data directly from the sensor without relay (i.e., in a single hop), finally returns and uploads data to the data sink. Since data packets are gathered directly without relay and collision, the lifetime of sensors is expected to be prolonged, and sensors can be made very simple and inexpensive. We focus on the problem of minimizing the length of each data gathering tour and refer to this problem as the single-hop data gathering problem, or SHDGP for short. We first formalize the SHDGP problem into a mixed integer program and prove its NP-hardness. We then present a heuristic tour-planning algorithm for a single M-collector. For the applications with a strict distance/time constraint for each data gathering tour, we also utilize multiple M-collectors to traverse through several shorter sub-tours concurrently to satisfy the distance/time constraint. Our single-hop data gathering scheme will improve the scalability and solve intrinsic problems of large scale homogeneous networks, and can be used in both connected networks and disconnected networks. The simulation results demonstrate that the new data gathering algorithm can greatly reduce the moving distance of the collectors compared to the covering line approximation algorithm, and is close to the optimal algorithm in small networks. In addition, the proposed data gathering scheme can prolong the network lifetime significantly compared to a network which has only a static data collector, or a network in which the mobile collector can only move along straight lines.

Unnamed aerial vehicle (UAV) can be employed to collect sensed data from sensor nodes (SNs) in wireless sensor networks, thanks to its high mobility and great maneuverability. However, it is very challenging for one single UAV with limited energy storage to complete the data collection task while guaranteeing the information freshness of the SNs. In this work, we investigate the AoI-optimal data collection problem in multi-UAV-aided wireless sensor networks, where the SNs may be densely located and multiple UAVs with limited energy supply perform data collection collaboratively. In a dense network, the communication ranges of the SNs might overlap with each other, and there is no need for the UAVs to fly over the SNs one by one and collect their sensing data sequentially. Instead, some points can be selected as data collection points (CPs) at which the UAVs collect data from multiple SNs simultaneously. Accordingly, the multi-UAV-aided data collection problem can be decomposed into three parts. Firstly, the positions of the CPs are determined and the SN-CP association is established based on graph theory. Secondly, the CPs are clustered based on the kernel K-means method with each cluster of CPs being visited by one UAV. Thirdly, the optimal flight trajectory of each UAV is found to minimize the the SNs' maximal age of information (AoI) subject to the limited energy capacity. Simulation results validate the effectiveness of our proposed data collection scheme.

Extending the network lifetime is a challenging problem in wireless sensor networks. Using additional mobile agents called data collectors in order to gather data from sensor nodes and carry them to the sink, reveals a convenient solution. Intuitively, a great sensor nodes energy conservation can be achieved by single hop data gathering strategies. However, these strategies may lead to significantly increase the data latency in the network. Previous studies propose to introduce local relay transmissions in order to reduce the data gathering time. Most of these strategies rely on a static parameter in order to choose the data collector trajectory. In this paper, we focus on a novel data gathering strategy that aims at dynamically selecting the data collector trajectory according to the required data delivery delay deadline. In other words, the data collector may change its trajectory according to the urgency of the gathered data. For this purpose, we propose an algorithm for coloring the network area into red and green disks, according to the delay deadline required by the sensor nodes. Only the red disks must be visited during a tour of the data collector. Simulation results demonstrate that the proposed scheme significantly outperforms static cluster based gathering strategies by efficiently adapting the tolerated data latency to the consumed energy rate.

The emerging Internet of Things (IoT)-based Wireless Sensor Networks (WSN) consist of small size of sensor nodes for monitoring and collecting data from environmental conditions and it transmits to other sensors through the internet. The major issues in WSN are energy constraints that degrade the efficient functioning and lifetime of WSN. Therefore, a novel technique called Gradient Enhanced Broken-stick Regressive Multivariate Artificial Fish Swarm Optimized Data Collection (GEBRMAFSODC) is introduced. The main objective of the GEBRMAFSODC technique for performing energy-efficient data collection with lesser delay , data loss. Smart cities improve effectiveness of different applications including public transport services. By applying this method, the resource efficient optimal path and the population of artificial fishes (i.e. sensor nodes) is randomly initialized in the search space. For each node, fitness is measured depend on multivariate function namely energy, bandwidth, and distance. The Gradient Enhanced Broken-stick Regression is applied to fitness estimation for analyzing the resources and finding the optimal results. Efficient neighboring nodes are selected to transmit the collected data to sink node via best path. Sink node perform as a data collector with better resource sensor nodes through lesser delay. Simulation is conducted in NS2 simulator using Warrigal Dataset and the performance is analyzed by various parameters namely energy consumption, data collection delay, throughput, and data loss rate based on number of data. The observed result shows the superior performance of the proposed GEBRMAFSODC technique with a higher delivery ratio, throughput by 10%, 48% and lesser loss, delay, and energy consumption by 53%, 37%, and 27% as compared to other related methods respectively.