Duc Van Le

VirFID: A Virtual Force (VF)-based Interest-Driven moving phenomenon monitoring scheme using multiple mobile sensor nodes

Abstract In this paper, we study mobile sensor network (MSN) architectures and algorithms for monitoring a moving phenomenon in an unknown and open area using a group of autonomous mobile sensor (MS) nodes. Monitoring a moving phenomenon involves challenges due to limited communication/sensing ranges of MS nodes, the phenomenon’s unpredictable changes in distribution and position, and the lack of information on the sensing area. To address the challenges and meet the objective of the maximization of weighted sensing coverage, we propose a novel scheme, namely VirFID (Virtual Force (VF)-based Interest-Driven moving phenomenon monitoring). In VirFID, MS nodes move toward the positions where more interesting sensing data can be obtained by utilizing the virtual force, which is calculated based on the distance between MS nodes and sensed values in the area of interest. MS nodes also perform network-wise information sharing to increase the weighted sensing coverage. Depending on the level of information used, three variants of VirFID are evaluated: VirFID-LIB (Local Information-Based), VirFID-GHL (Global Highest and Lowest), and VirFID-IBN (Interests at Boundary Nodes). In addition, an analytical model for estimating MSN speed is designed. Simulations are performed to compare the performance of three VirFID variants with other approaches. Our simulation results show that VirFID algorithms outperform other schemes in terms of the weighted coverage efficiency, and VirFID-IBN achieves the highest weighted coverage efficiency among VirFID variants.

HiCoDG: A Hierarchical Data-Gathering Scheme Using Cooperative Multiple Mobile Elements

In this paper, we study mobile element (ME)-based data-gathering schemes in wireless sensor networks. Due to the physical speed limits of mobile elements, the existing data-gathering schemes that use mobile elements can suffer from high data-gathering latency. In order to address this problem, this paper proposes a new hierarchical and cooperative data-gathering (HiCoDG) scheme that enables multiple mobile elements to cooperate with each other to collect and relay data. In HiCoDG, two types of mobile elements are used: the mobile collector (MC) and the mobile relay (MR). MCs collect data from sensors and forward them to the MR, which will deliver them to the sink. In this work, we also formulated an integer linear programming (ILP) optimization problem to find the optimal trajectories for MCs and the MR, such that the traveling distance of MEs is minimized. Two variants of HiCoDG, intermediate station (IS)-based and cooperative movement scheduling (CMS)-based, are proposed to facilitate cooperative data forwarding from MCs to the MR. An analytical model for estimating the average data-gathering latency in HiCoDG was also designed. Simulations were performed to compare the performance of the IS and CMS variants, as well as a multiple traveling salesman problem (mTSP)-based approach. The simulation results show that HiCoDG outperforms mTSP in terms of latency. The results also show that CMS can achieve the lowest latency with low energy consumption.

Environment Learning-Based Coverage Maximization With Connectivity Constraints in Mobile Sensor Networks

This paper takes into consideration the problems related to monitoring a phenomenon of interest in an unknown and open environment using multiple mobile sensor (MS) nodes. We propose an environment learning-based phenomenon monitoring system that iteratively learns about the environment and relocates MS nodes to optimal positions, where MS nodes can attain a high weighted sensing coverage and maintain network connectivity. In this paper, finding optimal positions for MS nodes is defined as the connectivity-constrained coverage maximization problem. An integer linear programming optimization formulation is proposed to find the solution. We also propose three heuristics algorithms to efficiently solve the connectivity-constrained coverage maximization problem. Simulation results show that the proposed algorithms outperform other approaches in terms of the weighted coverage efficiency and energy efficiency.

Maximization of the Supportable Number of Sensors in QoS-Aware Cluster-Based Underwater Acoustic Sensor Networks

This paper proposes a practical low-complexity MAC (medium access control) scheme for quality of service (QoS)-aware and cluster-based underwater acoustic sensor networks (UASN), in which the provision of differentiated QoS is required. In such a network, underwater sensors (U-sensor) in a cluster are divided into several classes, each of which has a different QoS requirement. The major problem considered in this paper is the maximization of the number of nodes that a cluster can accommodate while still providing the required QoS for each class in terms of the PDR (packet delivery ratio). In order to address the problem, we first estimate the packet delivery probability (PDP) and use it to formulate an optimization problem to determine the optimal value of the maximum packet retransmissions for each QoS class. The custom greedy and interior-point algorithms are used to find the optimal solutions, which are verified by extensive simulations. The simulation results show that, by solving the proposed optimization problem, the supportable number of underwater sensor nodes can be maximized while satisfying the QoS requirements for each class.

Symbiotic Sensing for Energy-Intensive Tasks in Large-Scale Mobile Sensing Applications

Energy consumption is a critical performance and user experience metric when developing mobile sensing applications, especially with the significantly growing number of sensing applications in recent years. As proposed a decade ago when mobile applications were still not popular and most mobile operating systems were single-tasking, conventional sensing paradigms such as opportunistic sensing and participatory sensing do not explore the relationship among concurrent applications for energy-intensive tasks. In this paper, inspired by social relationships among living creatures in nature, we propose a symbiotic sensing paradigm that can conserve energy, while maintaining equivalent performance to existing paradigms. The key idea is that sensing applications should cooperatively perform common tasks to avoid acquiring the same resources multiple times. By doing so, this sensing paradigm executes sensing tasks with very little extra resource consumption and, consequently, extends battery life. To evaluate and compare the symbiotic sensing paradigm with the existing ones, we develop mathematical models in terms of the completion probability and estimated energy consumption. The quantitative evaluation results using various parameters obtained from real datasets indicate that symbiotic sensing performs better than opportunistic sensing and participatory sensing in large-scale sensing applications, such as road condition monitoring, air pollution monitoring, and city noise monitoring.

A mobility prediction (MP)-based phenomenon monitoring in an unbounded area

The task of monitoring a moving phenomenon in an unbounded area using a mobile sensor network (MSN) brings out several challenges due to the high movement speed of phenomenon, the limited sensing/communication capabilities of mobile sensor nodes. To address the challenges and achieve a high weighted sensing coverage, in this paper we propose a monitoring algorithm, namely VirFID-MP (Virtual Force (VF)-based Interest-Driven moving phenomenon monitoring with Mobility Prediction). In VirFID-MP, the movement of phenomenon is first predicted based on its previous movements. Then, the predicted information is used to determine a global virtual force, which is utilized to speed up the MSN toward the moving phenomenon. Simulation results show that VirFID-MP outperforms original VirFID in terms of weighted coverage efficiency, when the MSN monitors a moving phenomenon.

A novel hierarchical cooperative data gathering architecture using multiple mobile elements

In this paper, we study the problem of data gathering in wireless sensor networks using cooperative mobile elements. Due to the limit on the physical speed of mobile elements, the existing data gathering schemes that use mobile element can suffer from a high data gathering latency. To address this problem, this paper proposes a new hierarchical and cooperative data gathering architecture (HCDGA) which enables multiple mobile elements to cooperate with each other to collect and relay the data. In HCDGA, two types of mobile elements are used, mobile collectors (MCs) and mobile relay (MR). MCs collect the data from sensors and the MR gathers the data from MCs and delivers the data to the sink. In this work, we first formulate ILP (Integer Linear Programming) optimization problem to find the optimal trajectories of MCs and the MR such that the energy consumption is minimized. Then, we extend the discussion to a cooperative movement scheduling algorithm for determining the optimal movement speeds of MCs and MR such that the minimum gathering latency can be achieved. The simulation results show that HCDGA outperforms multiple travelling salesman problem (mTSP) approach in term of the gathering latency, and the energy consumptions.

Towards an Online Seizure Advisory System—An Adaptive Seizure Prediction Framework Using Active Learning Heuristics

In the last decade, seizure prediction systems have gained a lot of attention because of their enormous potential to largely improve the quality-of-life of the epileptic patients. The accuracy of the prediction algorithms to detect seizure in real-world applications is largely limited because the brain signals are inherently uncertain and affected by various factors, such as environment, age, drug intake, etc., in addition to the internal artefacts that occur during the process of recording the brain signals. To deal with such ambiguity, researchers transitionally use active learning, which selects the ambiguous data to be annotated by an expert and updates the classification model dynamically. However, selecting the particular data from a pool of large ambiguous datasets to be labelled by an expert is still a challenging problem. In this paper, we propose an active learning-based prediction framework that aims to improve the accuracy of the prediction with a minimum number of labelled data. The core technique of our framework is employing the Bernoulli-Gaussian Mixture model (BGMM) to determine the feature samples that have the most ambiguity to be annotated by an expert. By doing so, our approach facilitates expert intervention as well as increasing medical reliability. We evaluate seven different classifiers in terms of the classification time and memory required. An active learning framework built on top of the best performing classifier is evaluated in terms of required annotation effort to achieve a high level of prediction accuracy. The results show that our approach can achieve the same accuracy as a Support Vector Machine (SVM) classifier using only 20% of the labelled data and also improve the prediction accuracy even under the noisy condition.

In-Pipe Wireless Communication for Underground Sampling and Testing

In this paper, we present an effective and low- cost wireless communication system for extremely long and narrow pipes that can replay the extant wire system in underground sensor network applications such as soil sampling and testing with the Cone Penetration Test (CPT), the most widely used underground sensor device. Different from existing in-pipe wireless techniques, we consider real-world pipelines that are very narrow and long. In particular, in our design data are first modulated at a commercial frequency and then converted to high frequency, between 14-15 GHz, to be transmitted along of the pipelines under the circular waveguide mode TM01. Especially, we design a cone-shaped antenna to overcome the aligning problem of feeds between the transmitter and receiver. To evaluate the applicability and efficiency of our design, we conduct realistic simulations as well as experiments with real prototypes. The results of experiments are consistent with our theoretical design and simulations and show that our proposed wireless system can transfer sensory data up to 20 m in narrow CPT pipes with a diameter of 17 mm when using the LoRa modulation with a transmitting power of 1 W, whereas existing underground radio techniques can transfer data from a depth of 2 m at maximum in the same condition. In our approach, it is also possible to add repeaters to extend the communication range when needed.

Ambient Sound-Based Collaborative Localization of Indeterministic Devices

Localization is essential in wireless sensor networks. To our knowledge, no prior work has utilized low-cost devices for collaborative localization based on only ambient sound, without the support of local infrastructure. The reason may be the fact that most low-cost devices are indeterministic and suffer from uncertain input latencies. This uncertainty makes accurate localization challenging. Therefore, we present a collaborative localization algorithm (Cooperative Localization on Android with ambient Sound Sources (CLASS)) that simultaneously localizes the position of indeterministic devices and ambient sound sources without local infrastructure. The CLASS algorithm deals with the uncertainty by splitting the devices into subsets so that outliers can be removed from the time difference of arrival values and localization results. Since Android is indeterministic, we select Android devices to evaluate our approach. The algorithm is evaluated with an outdoor experiment and achieves a mean Root Mean Square Error (RMSE) of 2.18 m with a standard deviation of 0.22 m. Estimated directions towards the sound sources have a mean RMSE of 17.5° and a standard deviation of 2.3°. These results show that it is feasible to simultaneously achieve a relative positioning of both devices and sound sources with sufficient accuracy, even when using non-deterministic devices and platforms, such as Android.