Data Collection Scheme Research Articles

Data Collection Schemes for Animal Monitoring Using WSNs-Assisted by UAVs: WSNs-Oriented or UAV-Oriented.

Wireless sensor networks (WSNs) and unmanned aerial vehicles (UAVs) have been used for monitoring animals but when their habitats have difficult access and are areas of a large expanse, remote monitoring by classic techniques becomes a difficult task. The use of traditional WSNs requires a restrictive number of hops in a multi-hoping routing scheme, traveling long distances to the sink node where data is stored by nodes and UAVs are used to collect data by visiting each node. However, the use of UAVs is not straightforward since the energy balance between the WSN and UAV has to be carefully calibrated. Building on this, we propose two data collection schemes in clustered based WSNs: (1) WSN oriented and (2) UAV oriented. In the former, nodes within each cluster member (CM), send information to their cluster head (CH) and for recollection, the UAV visits all CHs. As the UAV visits many CHs the flight time is increased. In the latter, all CHs send data from their CMs to a sink node, hence, the UAV only visits this node, reducing the flying time but with a higher system energy cost. To find the most suitable scheme for different monitoring conditions in terms of the average energy consumption and the buffer capacity of the system, we develop a mathematical model that considers both the dynamics of the WSN along with the UAV.

A robust data‐worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning

AbstractAs the collection of soil moisture data is often costly, it is essential to implement data‐worth analysis in advance to obtain a cost‐effective data collection scheme. In previous data‐worth analysis, the model structural error is often neglected. In this paper, we propose a robust data‐worth analysis framework based on a hybrid data assimilation method. By constructing Gaussian process (GP) error model, this study attempts to alleviate biased data‐worth assessments caused by unknown model structural errors, and to excavate complementary values of multisource data without resorting to multiple governing equations. The results demonstrated that this proposed framework effectively identified and compensated for complex model structural errors. By training prior data, more accurate potential observations were obtained and data‐worth estimation accuracy was improved. The scenario diversity played a crucial role in establishing an effective GP training system. The integration of soil temperature into GP training unraveled new information and improved the data‐worth estimation. Instead of traditional evapotranspiration calculations, the direct inclusion of easy‐to‐obtain meteorological data into GP training yielded better data‐worth assessment.

The Method of Data Collection Based on Multiple Mobile Nodes for Wireless Sensor Network

In traditional wireless sensor networks (WSN), data is transmitted to base stations through multiple-hop. However, multi-hop transmission will cause problems such as energy holes, uneven energy consumption, and unreliable data transmission. Aiming at the defects of traditional networks, this paper proposes a scheme for collaborative data collection using multiple mobile nodes (MN) as sink nodes. In this scenario, an important issue is how to reasonably plan the data collection path of each mobile node under a series of constraints such as energy. This paper studies routing strategy and path planning. Firstly, a dynamic clustering algorithm is used to cluster the randomly arranged sensor nodes, and then a sensor node with higher energy is manually arranged at the virtual cluster center generated by the clustering algorithm as a cluster head node to establish a data collection cluster. Then the monitoring area is divided into several parts according to the number of MN, so that the MN traverses the cluster head nodes in the respective monitoring areas for data collection. At the same time, in order to enable the MN to complete data collection within the energy limit and improve the path balance, a path-based path equalization algorithm (PEABR) is proposed to adjust the path of the MN, further reduce and equalize the path length of the MN to satisfy the constraint conditions, and optimize the path planning scheme. Finally, simulation was carried out by using Matlab simulator, and the simulation experiments which were performed in a laboratory environment verified the feasibility and effectiveness of the algorithm.

Optimum data collection and fusion schemes in WBSN

In this paper first we define our strategies aiming at minimising number of communicated data while keeping data integrity in wireless body sensor networks (WBSNs). In this way, we introduce modified Fisher test, develop Spline interpolation as the behaviour function and define controlling parameters. To achieve at significant results, we propose three efficient algorithms. Furthermore, at the second step, we represent our method to calculate the priority of vital sign data packets. For this purpose, we employ Spline interpolation function and define six new controlling parameters. At the third step, for correct inference of patient's situations we introduce our method which develops adaptive neuro fuzzy inference system (ANFIS) with cross-validation. Simulation results in MATLAB R2018b demonstrate the optimum performance of our schemes for number of communicated data, network lifetime, priority based data communication and also correct inference of patient's situations.

Optimum data collection and fusion schemes in WBSN

In this paper first we define our strategies aiming at minimising number of communicated data while keeping data integrity in wireless body sensor networks (WBSNs). In this way, we introduce modified Fisher test, develop Spline interpolation as the behaviour function and define controlling parameters. To achieve at significant results, we propose three efficient algorithms. Furthermore, at the second step, we represent our method to calculate the priority of vital sign data packets. For this purpose, we employ Spline interpolation function and define six new controlling parameters. At the third step, for correct inference of patient's situations we introduce our method which develops adaptive neuro fuzzy inference system (ANFIS) with cross-validation. Simulation results in MATLAB R2018b demonstrate the optimum performance of our schemes for number of communicated data, network lifetime, priority based data communication and also correct inference of patient's situations.

Optimal Data Collection for Mobile Crowdsensing Over Integrated Cellular and Opportunistic Networks

One of the main challenges that mobile crowdsensing systems must solve is reducing data collection costs while still holding high data delivery probability. Compared with cellular networks, opportunistic networks can significantly reduce data transfer costs at the cost of damaging data delivery probability. This paper proposes an optimal data collection scheme for mobile crowdsensing, which utilizes integrated cellular and opportunistic networks to implement data collection. We use data collecting path to describe how the sensing data are collected and sent to the back-end platform, though cellular networks directly or through multi-hop opportunistic networks. An optimal data collection problem is then formulated as choosing specific data collecting paths from candidate path set to minimize the total crowdsensing cost under the data delivery constraints, which can be considered as a minimum set covering problem. To solve this NP-hard problem, we design and implement a greedy heuristic algorithm that constructs the solution in multiple steps by making a locally optimal decision in each step. We conduct extensive simulations based on three real-world traces: Cambridge, Infocom06, and UPB. The results show that, compared with other data collection approaches, our approach achieves a better tradeoff between cost and data delivery.

Minimizing Data Collection Time With Collaborative UAVs in Wireless Sensor Networks

Unmanned aerial vehicle (UAV) data collection is a promising research direction that can be applied to many practical scenarios. Due to the limited energy of sensors in wireless sensor networks (WSN), UAV, which is considered as a mobile fusion center, can effectively prolong the lifetime of sensor via supporting communication with the sensor directly. However, since the UAV's energy constrained, it is necessary that multiple UAVs provide data collection to sensors in large areas. In this paper, we consider a scenario where multiple UAVs collect data from a set of two-dimensional distributed sensors. The UAV can communicate with sensors while flying or hovering. The goal is to minimize the total time that all UAVs from data center and return to data center after completing all collection tasks, while giving each sensor a certain amount of data and energy. The problem of minimizing the task completion time of multiple UAVs is still a big challenge. We solve the multi-UAV problem by jointly optimizing UAV-sensor association mechanism and data collection method of UAVs. The numerical results show that the proposed multi-UAV data collection scheme shortens the task completion time.

Rapid Re-Identification Risk Assessment for Anonymous Data Set in Mobile Multimedia Scene

Ubiquitous mobile multimedia applications bring great convenience to users. However, when enjoying mobile multimedia services, users provide personal data to service platforms. Although the service platforms always claim that the collected personal data are de-identified, the risk of re-identifying users through linkage attacks still exists and is incalculable. This paper proposes a rapid prediction model for the overall re-identification risk based on the statistics of data sets (i.e., the number of individuals, number of attributes, distribution of attribute values, and attribute dependency). Our proposed model reveals the impact of statistics on the overall re-identification risk and adopts random sampling and semi-random sampling methods to predict the overall re-identification risk of data sets with and without strong dependency ordered attribute pairs. Experimental results show that for the data sets without strong dependency ordered attribute pairs, the random sampling method has a high prediction accuracy (the prediction error is less than 0.05). For the data sets with strong dependency ordered attribute pairs, the semi-random sampling method has a high prediction accuracy (the prediction error is less than 0.09). Exploiting our model, governments and individuals can quickly assess the privacy leakage risk of their data sets, given only the statistic of the data sets. Besides, this model can also evaluate the privacy risk of data collection schemes in advance according to historical statistics, and identify suspected services.

A Historical Overview of Khat Production in Ethiopia

This paper tries to study the history of Khat production and expansion in Ethiopia. Khat has been consumed in Ethiopia for long period of time. Some sources moreover, mention Ethiopia as the origin of Khat production and the country that takes the lion share of Khat export to the rest of the world currently. The above mentioned notion has triggered the researcher to look at the history of this herbal and its production, consumption and contemporary use in Ethiopia. Different qualitative data collection techniques have been employed to get in to the bottom of the abovementioned topic. Here the researcher has conducted interview with elders who are believed to narrate the history of Khat, farmers who primary production has been Khat and consumers who use to consume Khat for long period of time. Moreover, secondary source of data like books, articles and term papers are also used to grasp the necessary information. After extensive data collection scheme and analysis, the researcher has reached into a conclusion that ancient Ethiopian rulers have been presented Khat as a gift for leaders of the south Arabia and this led the researcher to conclude that Ethiopia is one of the origin places of Khat if not the only country. Ethiopia is still the biggest Khat producing country with immense varieties that are mentioned inside this paper. Ethiopia could also be mentioned as the country where Khat is consumed immensely.

Cooperative mobile data collection in smart cities

Smart cities are driven by huge amount of data collected from sensors deployed across the city. Sensors typically form a multi-hop network with a base station (BS ) in order to send their data to the command and control center. However, sparse deployment of sensors can leave subsets of the network partitioned from the rest of the network. In such a case, isolated partitions cannot forward their data to the BS . Consequently, network coverage and data fidelity decline. A possible solution to link partitions and provide connectivity is employing mobile data collectors (MDCs). A smart vehicle supporting wireless communication can act as an MDC and carry data between sensors and the BS . Using a single MDC extends the average tour length. To minimize the maximum tour length, multiple MDCs can be employed. To identify sensors to be visited by each MDC, this paper clusters partitions as many as the number of MDCs and assigns an MDC for each cluster. Then two different cooperative data collection schemes are considered based on the availability of inter-MDC data exchange. If MDCs collaborate in data delivery, they meet at certain meeting points for data exchange. Such a cooperation avoids the requirement of visiting the BS for some MDCs and reduces tour lengths. On the other hand, MDCs closer to the BS can experience data loss due to buffer overflow given the higher volume of the accumulated data. Presented approaches are evaluated in terms of maximum tour length, data latency, and data loss. The smart city application is simulated with deployment of sensors on certain amenity types. Geographic data is obtained from a volunteered geographic information system and MDC mobility is restricted with the road network. Obtained results indicate that MDC cooperation decreases maximum tour length at the expense of increased rate of data loss and data latency.

Covid-19 risk assessment in public transport using ambient sensor data and wireless communications

Covid-19 causes one of the most alarming global health and economic crises in modern times. Countries around the world establish different preventing measures to stop or control Covid-19 spread. The goal of this paper is to present methods for the evaluation of indoor air quality in public transport to assess the risk of contracting Covid19. The first part of the paper involves investigating the relationship between Covid-19 and various factors affecting indoor air quality. The focus of this paper relies on exploring existing methods to estimate the number of occupants in public transport. It is known that increased occupancy rate increases the possibility of contamination as well as indoor carbon dioxide concentration. Wireless data collection schemes will be defined that can collect data from public transportation. Collected data are envisioned to be stored in the cloud for data analytics. We will present novel methods to analyze the collected data by considering the historical data and estimate the virus contagion risk level for each public transportation vehicle in service. The methodology is expected to be applicable for other airborne diseases as well. Real-time risk levels of public transportation vehicles will be available through a mobile application so that people can choose their mode of transportation accordingly.

Asynchronous Episodic Deep Deterministic Policy Gradient: Toward Continuous Control in Computationally Complex Environments.

Deep deterministic policy gradient (DDPG) has been proved to be a successful reinforcement learning (RL) algorithm for continuous control tasks. However, DDPG still suffers from data insufficiency and training inefficiency, especially, in computationally complex environments. In this article, we propose asynchronous episodic DDPG (AE-DDPG), as an expansion of DDPG, which can achieve more effective learning with less training time required. First, we design a modified scheme for data collection in an asynchronous fashion. Generally, for asynchronous RL algorithms, sample efficiency or/and training stability diminish as the degree of parallelism increases. We consider this problem from the perspectives of both data generation and data utilization. In detail, we redesign experience replay by introducing the idea of episodic control so that the agent can latch on good trajectories rapidly. In addition, we also inject a new type of noise in action space to enrich the exploration behaviors. Experiments demonstrate that our AE-DDPG achieves higher rewards and requires less time consumption than most popular RL algorithms in learning to run task which has a computationally complex environment. Not limited to the control tasks in the computationally complex environments, AE-DDPG also achieves higher rewards and two-fold to four-fold improvement in sample efficiency on average compared with other variants of DDPG in MuJoCo environments. Furthermore, we verify the effectiveness of each proposed technique component through abundant ablation study.

Local Aggregation Scheme for Data Collection in Periodic Sensor Network

Data aggregation is an important technique for data collection & aggregation in WSN where sensor nodes sense the raw data and sends the aggregated data to the sink node. In a cluster based periodic network, sensor node senses the data on a specific time interval, performs local aggregation and send aggregated data to Cluster Head (CH). Various Local aggregation algorithms are used to remove redundant data at sensor nodes but local outlier detection problem is still unsolved. Therefore, a local aggregation algorithm has been proposed which uses the temporal correlation property of WSN to eliminate redundant and local outlier data which improves the data sent ratio and data quality. Sensor measurement is collected at different time interval of a sensor, exhibits temporal correlation because measurements varies with small or same difference (δ) and measurements are treated as similar measurements. In proposed local aggregation approach, each sensor node finds similar measurements of sensors with their frequency (number of occurrence) in a specific time interval (Temporal correlation). Set having higher frequency is selected and transmitted the average values of measurements that lie in the selected set to the cluster head. If sensors don’t detect any reading between intervals it simple send a message ‘data not found’ instead of sending empty set. In this way we delete redundant and local outliers. The experimental result shows that algorithm improves the data quality and data sent ratio by eliminating redundant data and local outliers.

TrustData: Trustworthy and Secured Data Collection for Event Detection in Industrial Cyber-Physical System

In this article, an industrial cyber-physical system (ICPS) is utilized for monitoring critical events such as structural equipment conditions in industrial environments. Such a system can easily be a point of attraction for the cyberattackers, in addition to system faults, severe resource constraints (e.g., bandwidth and energy), and environmental problems. This makes data collection in the ICPS untrustworthy, even the data are altered after the data forwarding. Without validating this before data aggregation, detection of an event through the aggregation in the ICPS can be difficult. This article introduces TrustData, a scheme for high-quality data collection for event detection in the ICPS, referred to as “Trust worthy and secured Data collection” scheme. It alleviates authentic data for accumulation at groups of sensor devices in the ICPS. Based on the application requirements, a reduced quantity of data is delivered to an upstream node, say, a cluster head. We consider that these data might have sensitive information, which is vulnerable to being altered before/after transmission. The contribution of this article is threefold. First, we provide the concept of TrustData to verify whether or not the acquired data are trustworthy (unaltered) before transmission, and whether or not the transmitted data are secured (data privacy is preserved) before aggregation. Second, we utilize a general measurement model that helps to verify acquired signal untrustworthy before transmitting toward upstream nodes. Finally, we provide an extensive performance analysis through a real-world dataset, and our results prove the effectiveness of TrustData.

A Model-Checking-Based Framework for Analyzing Ambient Assisted Living Solutions.

Since modern ambient assisted living solutions integrate a multitude of assisted-living functionalities, out of which some are safety critical, it is desirable that these systems are analyzed at their design stage to detect possible errors. To achieve this, one needs suitable architectures that support the seamless design of the integrated assisted-living functions, as well as capabilities for the formal modeling and analysis of the architecture. In this paper, we attempt to address this need, by proposing a generic integrated ambient assisted living system architecture, consisting of sensors, data collection, local and cloud processing schemes, and an intelligent decision support system, which can be easily extended to suit specific architecture categories. Our solution is customizable, therefore, we show three instantiations of the generic model, as simple, intermediate, and complex configurations, respectively, and show how to analyze the first and third categories by model checking. Our approach starts by specifying the architecture, using an architecture description language, in our case, the Architecture Analysis and Design Language, which can also account for the probabilistic behavior of such systems, and captures the possibility of component failure. To enable formal analysis, we describe the semantics of the simple and complex architectures within the framework of timed automata. We show that the simple architecture is amenable to exhaustive model checking by employing the UPPAAL tool, whereas for the complex architecture we resort to statistical model checking for scalability reasons. In this case, we apply the statistical extension of UPPAAL, namely UPPAAL SMC. Our work paves the way for the development of formally assured future ambient assisted living solutions.

Driving safety efficiency benchmarking using smartphone data

This paper aims to provide a methodological framework for the comparative evaluation of driving safety efficiency based on Data Envelopment Analysis (DEA). The analysis considers each driver as a Decision Making Unit (DMU) and aims to provide a relative safety efficiency measure to compare different drivers based on their driving performance. The last is defined based on a set of driving analytics (e.g. distance travelled, speed, accelerations, braking, cornering and smartphone usage) collected using an innovative data collection scheme, which is based on the continuous recording of driving behavior analytics in real time, using smartphone device sensors. Safety efficiency is examined in terms of speed limit violation, driving distraction, aggressiveness and safety on urban, rural and highway road and in an overall model. DEA models are identifying the most efficient drivers that lie on the efficiency frontier and act as peers for the rest of the non-efficient drivers. The proposed methodological framework is tested on data from fifty-six (56) drivers during a 7-months period. Findings help distinguish the most efficient drivers from those that are less efficient. Moreover, the efficient level of inputs and outputs to switch from non-efficiency to the efficiency frontier is identified. Results also provide a potential for classification of the driving sample based on drivers’ comparative safety efficiency. The main characteristics of the most and less efficient drivers are consequently analyzed and presented. Most common inefficient driving practices are identified (aggressive, risky driving, etc.) and driving behavior is comparatively evaluated and analyzed.

$FREE$ —Fine-Grained Scheduling for Reliable and Energy-Efficient Data Collection in LoRaWAN

LoRaWAN promises to provide wide-area network access to low-cost devices that can operate for up to ten years on a single 1000-mAh battery. This makes LoRaWAN particularly suited for the data collection applications (e.g., monitoring applications), where device lifetime is a key performance metric. However, when supporting a large number of devices, LoRaWAN suffers from a scalability issue due to the high collision probability of its Aloha-based MAC layer. The performance worsens further when using acknowledged transmissions due to the duty-cycle restriction at the gateway. For this, we propose FREE , a fine-grained scheduling scheme for reliable and energy-efficient data collection in LoRaWAN. FREE takes advantage of applications that do not have hard delay requirements on data delivery by supporting the synchronized bulk data transmission. This means data are buffered for transmission in scheduled time slots instead of transmitted straight away. FREE allocates spreading factors, transmission powers, frequency channels, time slots, and schedules slots in frames for LoRaWAN end-devices. As a result, FREE overcomes the scalability problem of LoRaWAN by eliminating collisions and grouping acknowledgments. We evaluate the performance of FREE versus different legacy LoRaWAN configurations. The numerical results show that FREE scales well and achieves almost 100% data delivery and the device lifetime is estimated over ten years independent of traffic type and network size. In comparison to poor scalability, low data delivery and device lifetime of fewer than two years for acknowledged data traffic in the standard LoRaWAN configurations.

MDMS: Efficient and Privacy-Preserving Multidimension and Multisubset Data Collection for AMI Networks

Advanced metering infrastructure (AMI) networks allow utility companies to collect fine-grained power consumption data of electricity consumers for load monitoring and energy management. This brings serious privacy concerns since the fine-grained power consumption data can expose consumers’ activities. Privacy-preserving data aggregation techniques have been used to preserve consumers’ privacy while allowing the utility to obtain only the consumers total consumption. However, most of the existing schemes do not consider the multidimensional nature of power consumption in which electricity consumption can be categorized based on the consumption type. They also do not consider multisubset data collection in which the utility should be able to obtain the number of consumers whose consumption lies within a specific consumption range, and the overall consumption of each set of consumers. In this article, we propose an efficient and privacy-preserving multidimensional and multisubset data collection scheme, named “MDMS. ” In MDMS, the utility can obtain the total power consumption as well as the number of consumers of each subset in each dimension. In addition, for better scalability, MDMS allows the utility to delegate bill computation to the AMI networks’ gateways using the encrypted readings and following the dynamic prices in which electricity prices are different based on both the time and the consumption type. Moreover, MDMS uses lightweight operations in encryption, aggregation, and decryption resulting in low computation and communication overheads as given in our experimental results. Our security analysis demonstrates that MDMS is secure and can resist collusion attacks that aim to reveal the consumers’ readings.

Efficient Collection of Connected Vehicles Data With Precision Guarantees

Connected vehicles disseminate detailed data, including their position and speed, at a very high frequency. Such data can be used for accurate real-time analysis, prediction and control of transportation systems. The outstanding challenge for such analysis is how to continuously collect and process extremely large volumes of data. To address this challenge, efficient collection of data is critical to prevent overburdening the communication systems and overreaching computational and memory capacity. In this work, we propose an efficient data collection scheme that selects and transmits only a small subset of the data to alleviate data transmission burden. As a demonstration, we have used the proposed approach to select the data points to be transmitted from 10,000 connected vehicles trips available in the Safety Pilot Model Deployment dataset. The presented results show that collection ratio can be as small as 0.05 depending on the required precision. Moreover, a simulation study was performed to evaluate the travel time estimation accuracy using the proposed data collection approach. Results show that the proposed data collection approach can significantly improve travel time estimation accuracy.

The Importance of Infrastructures in the LDC’s Economic Sustainable Development

Infrastructures are crucial for sustainable growth and inclusive development of Less Developed Countries. The problem in hypothesis is the African infrastructure gap, that neither governments nor civil society organizations seems to want to face promoting investments in tangible programs that take into consideration such kinds of important elements aimed to an equal durable and sustainable improvement. The paper starts with a description of recent initiatives to scale up infrastructure investment in Africa. The methodology follows a scheme of hypothesis, observations experiences and data collection from ADB and others Institutions. This research tries to let understand what is the connection between development and infrastructures for human being. The paper then uses insights from the literature on informed versus arm’s length debt to discuss the structure of infrastructure financing. The economic growth and its goal, outlines the difference between structures and infrastructures. The main effort is how to give a weight to these latter in order to measure the empowerment of people that pass through their standard of living based on a system of strategic priorities centered on infrastructure package investments. Understanding the importance of infrastructures helps to satisfy needs and implement services that even if deemed not essential and substantial, are anyway most important to the growth of personal and social conditions. In this context the analysis is made on the difference between economic growth and development grounded on structure and infrastructure. The effort is to try to identify indicators able to evidence the contribution ratio of infrastructures to the development, how this can be measured and how they work in the different fields. The conclusion and implications are that to yield such potential global benefits, Africa’s industrialization would have to be underpinned by a robust infrastructure financing program. This requires a global finance pact among advanced and developing countries, a shift in strategic approaches, and new models of financing. Keywords: Africa, Infrastructure Finance, Economic growth, Development Banks, Integral development economy, Long-term Investors, Development indicators, Economy of Francesco, Lonergan’s new economic vision