Localized ant colony of robots for redeployment in wireless sensor networks

Sensor failures or oversupply in wireless sensor networks (WSNs), especially initial random deployment, create spare sensors (whose area is fully covered by other sensors) and sensing holes. We envision a team of robots to relocate sensors and improve their area coverage. Existing algorithms, including centralized ones and the only localized G-R3S2 [9], move only spare sensors and have limited improvement because non-spare sensors, with area coverage mostly overlapped by neighbour sensors, are not moved, and additional sensors are deployed to fill existing small holes. We propose a localized algorithm, called Localized Ant-based Sensor Relocation Algorithm with Greedy Walk (LASR-G), where each robot may carry at most one sensor and makes decision that depends only on locally detected information. In LASRG, each robot calculates corresponding pickup or dropping probability, and relocates sensor with currently low coverage contribution to another location where sensing hole would be significantly reduced. The basic algorithm optimizes only area coverage, while modified algorithm includes also the cost of robot movement. We compare LASR-G with G-R3S2, and examine both single robot and multi robots scenarios. The simulation results show the advantages of LASR-G over G-R3S2.

An alternative data collection scheduling scheme in wireless sensor networks

Despite significant advancements in wireless sensor networks (WSNs), energy conservation remains one of the most important research challenges. Recently, the problem of energy conservation has been addressed by applying mobile sink as an effective technique that can enhance efficiency of energy consumption in the networks. In this paper, the energy conservation problem is firstly formulated to maximize the lifetime of WSN subject to delay and node energy constraints. Then, to solve the defined energy conservation problem, a data collection scheduling with a mobile sink scheme is proposed. In the proposed approach, the sink movement is governed by a type-2 fuzzy controller to be located at the best location and time to collect sensory data. We conducted extensive experiments to study the effectiveness of the proposed protocol and compared it against the streaming data delivery (SDD) and virtual circle combined straight routing (VCCS) protocols. We observed that the proposed protocol outperforms both SDD and VCCS approaches by reducing energy consumption, minimize delays and enhance data collection quality.

A congestion control scheme based on fuzzy logic in wireless body area networks

One of the major challenges in healthcare wireless body area network (WBAN) applications is to control congestion. Unpredictable traffic load, many-to-one communication nature and limited bandwidth occupancy are among major reasons that can cause congestion in such applications. Congestion has negative impacts on the overall network performance such as packet losses, increasing end-to-end delay and wasting energy consumption due to a large number of retransmissions. In life-critical applications, any delay in transmitting vital signals may lead to death of a patient. Therefore, in order to enhance the network quality of service (QoS), developing a solution for congestion estimation and control is imperative. In this paper, we propose a new congestion detection and control protocol for remote monitoring of patients health status using WBANs. The proposed system is able to detect congestion by considering local information such as buffer capacity and node rate. In case of congestion, the proposed system differentiates between vital signals and assigns priorities to them based on their level of importance. As a result, the proposed approach provides a better quality of service for transmitting highly important vital signs.

Fault tolerance in data gathering wireless sensor networks

In data gathering wireless sensor networks, data loss often happens due to external faults such as random link faults and hazard node faults, since sensor nodes have constrained resources and are often deployed in inhospitable environments. However, already known fault tolerance mechanisms often bring new internal faults (e.g. out-of-power faults and collisions on wireless bandwidth) to the original network and dissipate lots of extra energy and time to reduce data loss. Therefore, we propose a novel Dual Cluster Heads Cooperation (CoDuch) scheme to tolerate external faults while introducing less internal faults and dissipating less extra energy and time. In CoDuch scheme, dual cluster heads cooperate with each other to reduce extra costs by sending only one copy of sensed data to the Base Station; also, dual cluster heads check errors with each other during the collecting data process. Two algorithms are developed based on the CoDuch scheme: CoDuch-l for tolerating link faults and CoDuch-b for tolerating both link faults and node faults; theory and experimental study validate their effectiveness and efficiency. © 2010 The Author Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.

Data mining via minimal spanning tree clustering for prolonging lifetime of wireless sensor networks

Clustering is applied in wireless sensor networks for increasing energy efficiency. Clustering methods in wireless sensor networks are different from those in traditional data mining systems. This paper proposes a novel clustering algorithm based on Minimal Spanning Tree (MST) and Maximum Energy resource on sensors named MSTME. Also, specified constrains of clustering in wireless sensor networks and several evaluation metrics are given. MSTME performs better than already known clustering methods of Low Energy Adaptive Clustering Hierarchy (LEACH) and Base Station Controlled Dynamic Clustering Protocol (BCDCP) in wireless sensor networks when they are evaluated by these evaluation metrics. Simulation results show MSTME increases energy efficiency and network lifetime compared with LEACH and BCDCP in two-hop and multi-hop networks, respectively. © World Scientific Publishing Company.

Dynamic minimal spanning tree routing protocol for large wireless sensor networks

Hundreds or thousands of wireless sensor nodes with limited energy resource are randomly scattered in the observation fields to extract the data messages for users. Because their energy resource cannot be recharged, energy efficiency becomes one of the most important problems. LEACH is an energy efficient protocol by grouping nodes into clusters and using cluster heads (CH) to fuse data before transmitting to the base station (BS). BCDCP improves LEACH by introducing a minimal spanning tree (MST) to connect CHs and adopting iterative cluster splitting algorithm to choose CHs or form clusters. This paper proposes another innovative cluster-based routing protocol named dynamic minimal spanning tree routing protocol (DMSTRP), which improves BCDCP by introducing MSTs instead of clubs to connect nodes in clusters. Simulation results show that DMSTRP excels LEACH and BCDCP in terms of both network lifetime and delay when the network size becomes large.

Network lifetime of application-specific randomly deployed wireless sensor networks in arbitrary sensor density

In many cases, sensors are randomly deployed in Wireless Sensor Networks (WSN), called Sensor-Randomly-Deployed WSN (SRD WSN). Several cluster-based routing protocols are provided to maximize network lifetime of SRD WSN in different sensor densities. LEACH performs better than direct routing in the density of 0.01. BCDCP excels LEACH in the density of 0.05. DMSTRP outperforms LEACH and BCDCP in the density of. However, simulation results under one or two kinds of sensor densities are not strong enough to prove the optimum of the routing protocols. In this paper, we give the general formulas to compute the network lifetimes of the above three routing protocols, discuss their optimal number of clusters, and compare their optimal network lifetime in arbitrary sensor densities. These formulas can provide more general design guidelines applicable to SRD WSN than simulation results under only one or two kinds of sensor densities. © 2007 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.

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.

Anomaly detection in wireless sensor networks in a non-stationary environment

Anomaly detection in a WSN is an important aspect of data analysis in order to identify data items that significantly differ from normal data. A characteristic of the data generated by a WSN is that the data distribution may alter over the lifetime of the network due to the changing nature of the phenomenon being observed. Anomaly detection techniques must be able to adapt to a non-stationary data distribution in order to perform optimally. In this survey, we provide a comprehensive overview of approaches to anomaly detection in a WSN and their operation in a non-stationary environment.

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. .

Streaming analysis in wireless sensor networks

Two new incremental models for online anomaly detection in data streams at nodes in wireless sensor networks are discussed. These models are incremental versions of a model that uses ellipsoids to detect first, second, and higher-ordered anomalies in arrears. The incremental versions can also be used this way but have additional capabilities offered by processing data incrementally as they arrive in time. Specifically, they can detect anomalies 'on-the-fly' in near real time. They can also be used to track temporal changes in near real-time because of sensor drift, cyclic variation, or seasonal changes. One of the new models has a mechanism that enables graceful degradation of inputs in the distant past (fading memory). Three real datasets from single sensors in deployed environmental monitoring networks are used to illustrate various facets of the new models. Examples compare the incremental version with the previous batch and dynamic models and show that the incremental versions can detect various types of dynamic anomalies in near real time.

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.