Connected P-percent coverage in wireless sensor networks based on degree constraint dominating set approach

In this paper, we propose an algorihm for conneced p-percent coverage probem in Wireless Sensor Networks(WSNs) to improve the over netork life time. In this work, we invstigae the p-pernt coverage problem(PCP) in WSNs which require % of n area should be monitored correctl and to find ou ny additional requirements of the connec p-percent coverge prom. We prose pDCDS algorith which is a learnin autmaton basd algorithm fr PCP pDCDS is a Degreconsained Connected Domining Se based algoithm whch detect the minimum numbe of des to monitor an area. The simulation results demonstrate hat pDCDS can remarkably improve the network lifetime.

Design and fabrication of a capacitance based wearable pressure sensor using e-textiles

This paper addresses the methods used for the design and fabrication of a capacitance based wearable pressure sensor fabricated using neoprene and (SAC) plated Nylon Fabric. The experimental set up for the pressure sensor is comprised of a shielded grid of sensing modules, a 555 timer based transduction circuitry, and an Arduino board measuring the frequency of signal to a corresponding pressure. The fundamental design parameters addressed during the development of the pressure sensor presented in this paper are based on size, simplicity, cost, adaptability, and scalability. The design approach adopted in this paper results in a sensor module that is less obtrusive, has a thinner and flexible profile, and its sensitivity is easily scalable for ‘smart’ product applications across industries associated to sports performance, ergonomics, rehabilitation, etc.

Rapid and sensitive ethanol sensor based on hollow Au/V2O5 nanotubes via emulsion-electrospinning route

Hollow electrospun V2O5 and Au/V2O5 nanotubes have been successfully synthesized by combining emulsion electrospinning (EE) and post calcination treatment. Immiscible polyvinyl pyrrolidone (PVP)/metal salts/dimethylformamide (DMF) solution and polystyrene (PS)/DMF solution are chosen for the EE to form PS PVP/metal salts) core-sheath nanofibers, in which PS nanorods were formed and encapsulated within in the PVP/metal salts nanofibers owing to the stretching forces and de-emulsified force during the electrospinning. Excellent sensitivity and rapid response-recovery behaviors against ethanol have been successfully achieved based on our hollow ceramic (V2O5 and Au/V2O5) nanotubes.

Market-based sensor relocation by robot team inwireless sensor networks

Randomly scattered sensors may cause sensing holes and redundant sensors. In carrier-based sensor relocation, mobile robots (with limited capacity to carry sensors) pick up additional or redundant sensors and relocate them at sensing holes. In the only known localized algorithm, robots randomly traverse field and act based on identified pair of spare sensor and coverage hole. We propose a Market-based Sensor Relocation (MSR) algorithm, which optimizes sensor deployment location, and introduces bidding and coordinating among neighboring robots. Sensors along the boundary of each hole elect one of them as the representative, which bids to neighboring robots for hole filling service. Each robot randomly explores by applying Least Recently Visited policy. It chooses the best bid according to Cost over Progress ratio and fetches a spare sensor nearby to cover the corresponding sensing hole. Robots within communication range share their tasks to search for better possible solutions. Simulation shows that MSR outperforms the existing competing algorithm G-R3S2 significantly on total robot traversed path and energy, and time to cover holes, slightly on number of sensors needed to cover the hole and number of sensor messages for bidding and deployment location sharing.

RSSI-based localization of a wireless sensor node with a flying robot

We consider the problem of navigating a ying robot to a specific sensor node within a wireless sensor network. This target sensor node periodically sends out beacons. The robot is capable of sensing the received signal strength of each received beacon (RSSI measurements). Existing approaches for solving the sensor spotting problem with RSSI measurements do not deal with noisy channel conditions and/or heavily depend on additional hardware capabilities. In this work we reduce RSSI uctuations due to noise by continuously sampling RSSI values and maintaining an exponential moving average (EMA). The EMA values enable us to detect significant decrease of the received signal strength. In this case it is reasoned that the robot is moving away from the sensor. We present two basic variants to decide a new moving direction when the robot moves away from the sensor. Our simulations show that our approaches outperform competing algorithms in terms of success rate and ight time. Infield experiments with real hardware, a ying robocopter successfully and quickly landed near a sensor placed in an outdoor test environment. Traces show robustness to additional environmental factors not accounted for in our simulations.

Inter-Frame Change Directing online clustering of multiple moving objects for video-based sensor networks

Recognition of multiple moving objects is a very important task for achieving user-cared knowledge to send to the base station in wireless video-based sensor networks. However, video based sensor nodes, which have constrained resources and produce huge amount of video streams continuously, bring a challenge to segment multiple moving objects from the video stream online. Traditional efficient clustering algorithms such as DBSCAN cannot run time-efficiently and even fail to run on limited memory space on sensor nodes, because the number of pixel points is too huge. This paper provides a novel algorithm named Inter-Frame Change Directing Online clustering (IFCDO clustering) for segmenting multiple moving objects from video stream on sensor nodes. IFCDO clustering only needs to group inter-frame different pixels, thus it reduces both space and time complexity while achieves robust clusters the same as DBSCAN. Experiment results show IFCDO clustering excels DBSCAN in terms of both time and space efficiency. © 2008 IEEE.

Wireless video-based sensor networks for surveillance of residential districts

Compared to traditional wired video sensor networks to supervise a residential district, Wireless Video-based Sensor Networks (WVSN) can provide more detail and precise information while reduce the cost. However, state-of-the-art low cost wireless video-based sensors have very constrained resources such as low bandwidth, small storage, limited processing capability, and limited energy resource. Also, due to the special sensing range of video-based sensors, cluster-based routing is not as effective as it apply to traditional sensor networks. This paper provides a novel real-time change mining algorithm based on an extracted profile model of moving objects learnt from frog's eyes. Example analysis shows the extracted profile would not miss any important semantic images to send to the Base Station for further hazards detection, while efficiently reducing futile video stream data to the degree that nowadays wireless video sensor can realize. Thus it makes WVSN available to surveillance of residential districts.

Energy-efficiency analysis of cluster-based routing protocols in wireless sensor network

This paper extends the conditions of the cluster-based routing protocols in terms of general algorithm complexity of data fusion, general compressing ratio of data fusion, and network area with long distance. Corresponding three general evaluation methods to evaluate the energy efficiency of the cluster-based routing protocols such as LEACH, PEGASIS, and BCDCP are provided. Moreover, three facts are found in them: (1) High-level software energy macro model is used to compute the energy dissipation of general data fusion software and make the constant value of energy dissipation of 1-bit data fusion an especial instance. (2) Multi-hop energy efficiency is related to the radio hardware parameters and the dynamic topology of network and the above protocols do not exploit the best use of the energy efficiency of multi-hop scheme. (3) High-energy dissipation non-cluster-head nodes, whose number changes with the density of the sensor nodes in clusters, worsen the death of nodes. The numerical results of experiments reprove these discoveries. Furthermore, they provide helpful guide for improving the above routing protocols to extent their application ranges.

Geospatial estimation-based auto drift correction in wireless sensor networks

Wireless sensor networks are often deployed in large numbers, over a large geographical region, in order to monitor the phenomena of interest. Sensors used in the sensor networks often suffer from random or systematic errors such as drift and bias. Even if they are calibrated at the time of deployment, they tend to drift as time progresses. Consequently, the progressive manual calibration of such a large-scale sensor network becomes impossible in practice. In this article, we address this challenge by proposing a collaborative framework to automatically detect and correct the drift in order to keep the data collected from these networks reliable. We propose a novel scheme that uses geospatial estimation-based interpolation techniques on measurements from neighboring sensors to collaboratively predict the value of phenomenon being observed. The predicted values are then used iteratively to correct the sensor drift by means of a Kalman filter. Our scheme can be implemented in a centralized as well as distributed manner to detect and correct the drift generated in the sensors. For centralized implementation of our scheme, we compare several krigingand nonkriging-based geospatial estimation techniques in combination with the Kalman filter, and show the superiority of the kriging-based methods in detecting and correcting the drift. To demonstrate the applicability of our distributed approach on a real world application scenario, we implement our algorithm on a network consisting of Wireless Sensor Network (WSN) hardware. We further evaluate single as well as multiple drifting sensor scenarios to show the effectiveness of our algorithm for detecting and correcting drift. Further, we address the issue of high power usage for data transmission among neighboring nodes leading to low network lifetime for the distributed approach by proposing two power saving schemes. Moreover, we compare our algorithm with a blind calibration scheme in the literature and demonstrate its superiority in detecting both linear and nonlinear drifts.

Hyperspherical cluster based distributed anomaly detection in wireless sensor networks

This article describes a distributed hyperspherical cluster based algorithm for identifying anomalies in measurements from a wireless sensor network, and an implementation on a real wireless sensor network testbed. The communication overhead incurred in the network is minimised by clustering sensor measurements and merging clusters before sending a compact description of the clusters to other nodes. An evaluation on several real and synthetic datasets demonstrates that the distributed hyperspherical cluster-based scheme achieves comparable detection accuracy with a significant reduction in communication overhead compared to a centralised scheme, where all the sensor node measurements are communicated to a central node for processing. .

Network coding based converge-cast scheme in wireless sensor networks

The converge-cast in wireless sensor networks (WSNs) is widely applied in many fields such as medical applications and the environmental monitoring. WSNs expect not only providing routing with high throughput but also achieving efficient energy saving. Network coding is one of the most promising techniques to reduce the energy consumption. By maximizing the encoding number, the message capacity per package can be extended to the most efficient condition. Thus, many researchers have focused their work on this field. Nevertheless, the packages sent by the outer nodes need to be temporary stored and delayed in order to maximize the encoding number. To find out the balance between inserting the delay time and maximizing the encoding number, a Converge-cast Scheme based on data collection rate prediction (CSRP) is proposed in this paper. To avoid producing the outdated information, a prediction method based on Modifying Index Curve Model is presented to deal with the dynamic data collection rate of every sensor in WSNs. Furthermore, a novel coding conditions based on CDS is proposed to increase the coding opportunity and to solve the collision problems. The corresponding analysis and experimental results indicate that the feasibility and efficiency of the CSRP is better than normal conditions without the prediction.