Sparse Sensor Networks Research Articles

A Node Deployment Strategy for Efficient Sensing with Mobile Sensors in Sparse Sensor Networks

A Node Deployment Strategy for Efficient Sensing with Mobile Sensors in Sparse Sensor Networks

Area Coverage Vs Event Detection in Mixed Sensor Networks

AbstractThis paper considers the problem of improving the monitoring capability of a sparse stationary sensor network by using mobile sensor nodes and addresses the trade off between area coverage and fast event detection. Though these are two similar objectives, they can lead to two different strategies. In this context, this paper derives the optimal search path strategy for minimizing the event detection time in the case of two uncovered regions. Finding the optimal searching path is an NP-hard problem, which implies that we can not find the optimal solution of an arbitrary problem instance in reasonable time, due to large number of uncovered regions and mobile nodes. Thus using the principles of the derived solution, an efficient and distributed path planning strategy is also presented for the general case. Simulation results are included to demonstrate the effectiveness of the proposed approach.

A Hybrid Adaptive Protocol for Reliable Data Delivery in WSNs with Multiple Mobile Sinks

In this paper, we deal with reliable and energy-efficient data delivery in sparse wireless sensor networks (WSNs) with multiple mobile sinks (MSs). This is a critical task, especially when MSs move randomly, as interactions with sensor nodes are unpredictable, typically of short duration and affected by message losses. In addition, multiple MSs can be simultaneously present in the sensor contact area making the minimum energy data delivery a complex optimization problem. To solve the above issues, in this paper we propose a novel protocol that efficiently combines erasure coding with an Automatic Repeat reQuest (ARQ) scheme. The key features of the proposed protocol are as follows: (i) the use of redundancy to cope efficiently with message losses in the multicast environment and (ii) the ability of adapting the level of redundancy based on feedbacks sent back by MSs through acks. We observed by simulation that our protocol outperforms an alternative protocol that relies only on an ARQ scheme, even when there is a single MS. We also validated our simulation results through a set of experimental measurements based on real sensor nodes. Our results show that the adoption of encoding techniques increases the lifetime of the sensor in the range (40–55%) compared with standard simple ARQ approaches when applied to WSNs with MSs.

Multi-site damage localization in anisotropic plate-like structures using an active guidedwave structural health monitoring system

A new approach for structural health monitoring using guided waves in plate-like structureshas been developed. In contrast to previous approaches, which mainly focused on isotropicor quasi-isotropic plates, the proposed algorithm does not assume any simplificationsregarding anisotropic wave propagation. Thus, it can be used to improve the probability ofdetection.In this paper the mathematical background for damage localization in anisotropic plateswill be introduced. This is an extension of the widely known ellipse method. The formalismis based on a distributed sensor network, where each piezoelectric sensor acts in turn as anactuator. The automatic extraction of the onset time of the first waveform in thedifferential signal in combination with a statistical post-processing via a two-dimensionalprobability density function and the application of the expectation-maximization algorithmallows a completely automatic localization procedure. Thus, multiple damages can beidentified at the same time.The present study uses ultrasonic signals provided by the spectral element method. Thissimulation approach shows good agreement with experimental measurements. A local linearneural network is used to model the nonlinear dispersion curves. The benefit of using aneural network approach is to increase the angular resolution that results from the sparsesensor network. Furthermore, it can be used to shorten the computational time for thedamage localization procedure.

LDB: Localization with Directional Beacons for Sparse 3D Underwater Acoustic Sensor Networks

In this paper, we propose a novel distributed localization scheme LDB, a 3D localization scheme with directional beacons for Underwater Acoustic Sensor Networks (UWA-SNs). LDB localizes sensor nodes using an Autonomous Underwater Vehicle (AUV) as a mobile beacon sender. Mounted with a directional transceiver which creates conical shaped directional acoustic beam, the AUV patrols over the 3D deployment volume with predefined trajectory sending beacons with constant interval towards the sensor nodes. By listening two or more beacons sent from the AUV, the nodes can localize themselves silently. Through theoretical analysis, we provide the upper bound of the estimation error of the scheme. We also evaluate the scheme by simulations and the results show that our scheme can achieve a high localization accuracy, even in sparse networks.

Sensor Relocation for Emergent Data Acquisition in Sparse Mobile Sensor Networks

In this paper, we study the problem of sensor relocation for emergent data acquisition (initiated by a base station) in sparse mobile sensor networks. We propose a distributed scheme called BRIDGE that relocates mobile sensors to fulfill an emergent data acquisition task with the objective to minimize the task completion time. BRIDGE gradually finds a sensor that is close to the task location and relocates that sensor to the task location, and at the same time relocates some other sensors to connect that sensor to the base station. BRIDGE exploits the encountered sensors during relocation, and handles the challenges caused by intermittent connections. Our extensive performance study shows the effectiveness of our proposed scheme.

An Effective Mobile Sensor Control Method for Sparse Sensor Networks

In this paper, we propose an effective mobile sensor control method, named DATFM (Data Acquisition and Transmission with Fixed and Mobile node) for sparse sensor networks. DATFM uses two types of sensor nodes, fixed node and mobile node. The data acquired by nodes are accumulated on a fixed node before being transferred to the sink node. In addition, DATFM transfers the accumulated data efficiently by constructing a communication route of multiple mobile nodes between fixed nodes. We also conduct simulation experiments to evaluate the performance of DATFM.

On the efficient and fast response for sensor deployment in sparse wireless sensor networks

Wireless sensor networks have recently become new techniques and popular research issues. A wireless sensor network consists of a large number of sensor nodes that have the capabilities of sensing, computing and wireless transmission. Wireless sensor networks (namely WSNs) assist people in working under dangerous environments, provide long-term target observations and track on moving objects. Consequently, WSNs decrease risk and increase efficiency. Although WSNs have been studied extensively, several problems should be addressed, such as sensor-deployment policy, data aggregation/fusion issue, and data transmission issue. An efficient sensor-deployment approach could decrease cost, minimize transmission delay and reduce time complexity. Most studies have proposed the probability-based sensor-deployment policies to monitor an overall area. However, not the entire network is interested to be sensed/monitored. Monitoring of an entire area brings several disadvantages: (1) high cost of placing large number of sensors, (2) long delay of data transmission, (3) slow response and (4) unnecessary data aggregation. Furthermore, previous works were lack of considering the difference between the sensing and the transmission radii, and then yield inaccurate analysis. This work thus proposes an efficient sensor placement approach (namely ESP) for a sparse interested area with considering of obstructers that block the data transmission and sensing signal. Additionally, the issue of different radii of sensing and transmission is analyzed in detail. Numerical results demonstrate that the proposed ESP approach requires the least number of sensor nodes under various network sizes and different number of obstacles. Simulation results indicate that the number of sensor nodes decreases when the sensing or transmission radius increases. The running time of ESP, O( K 2), is also analyzed, which is better than that of the probability-based approaches, O( N 2), where K is the number of interested grids and N is the number of grids.

Protocol for the application of cooperative MIMO based on clustering in sparse wireless sensor networks

Wireless sensor networks (WSN) using cooperative multiple-input multiple-output (MIMO) communication are effective tools to collect data in several environments. However, how to apply cooperative MIMO in WSN remains a critical challenge, especially in sparse WSN. In this article, a novel clustering scheme is proposed for the application of cooperative MIMO in sparse WSN by extending the traditional low-energy adaptive clustering hierarchy (LEACH) protocol. This clustering scheme solves the problem that the cluster heads (CH) cannot find enough secondary cluster heads (SCH), which are used to cooperate and inform multiple-antenna transmitters with CHs. On the basis of this protocol, the overall energy consumption of the networks model is developed, and the optimal number of CHs is obtained. The simulation results show that this protocol is feasible for the sparse WSN. The simulation results also illustrate that this protocol provides significant energy efficiencies, even after allowing for additional overheads.

Data MULEs: modeling and analysis of a three-tier architecture for sparse sensor networks

This paper presents and analyzes a three-tier architecture for collecting sensor data in sparse sensor networks. Our approach exploits the presence of mobile entities (called MULEs) present in the environment. When in close range, MULEs pick up data from the sensors, buffer it, and deliver it to wired access points. This can lead to substantial power savings at the sensors as they only have to transmit over a short-range. This paper focuses on a simple analytical model for understanding performance as system parameters are scaled. Our model assumes a two-dimensional random walk for mobility and incorporates key system variables such as number of MULEs, sensors and access points. The performance metrics observed are the data success rate (the fraction of generated data that reaches the access points), latency and the required buffer capacities on the sensors and the MULEs. The modeling and simulation results can be used for further analysis and provide certain guidelines for deployment of such systems.