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.

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.

Data processing of physiological sensor data and alarm determination utilising activity recognition

Current physiological sensors are passive and transmit sensed data to Monitoring centre (MC) through wireless body area network (WBAN) without processing data intelligently. We propose a solution to discern data requestors for prioritising and inferring data to reduce transactions and conserve battery power, which is important requirements of mobile health (mHealth). However, there is a problem for alarm determination without knowing the activity of the user. For example, 170 beats per minute of heart rate can be normal during exercising, however an alarm should be raised if this figure has been sensed during sleep. To solve this problem, we suggest utilising the existing activity recognition (AR) applications. Most of health related wearable devices include accelerometers along with physiological sensors. This paper presents a novel approach and solution to utilise physiological data with AR so that they can provide not only improved and efficient services such as alarm determination but also provide richer health information which may provide content for new markets as well as additional application services such as converged mobile health with aged care services. This has been verified by experimented tests using vital signs such as heart pulse rate, respiration rate and body temperature with a demonstrated outcome of AR accelerometer sensors integrated with an Android app.

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.

On the race of worms and patches: modeling the spread of information in wireless sensor networks

Sensor networks are a branch of distributed ad hoc networks with a broad range of applications in surveillance and environment monitoring. In these networks, message exchanges are carried out in a multi-hop manner. Due to resource constraints, security professionals often use lightweight protocols, which do not provide adequate security. Even in the absence of constraints, designing a foolproof set of protocols and codes is almost impossible. This leaves the door open to the worms that take advantage of the vulnerabilities to propagate via exploiting the multi-hop message exchange mechanism. This issue has drawn the attention of security researchers recently. In this paper, we investigate the propagation pattern of information in wireless sensor networks based on an extended theory of epidemiology. We develop a geographical susceptible-infective model for this purpose and analytically derive the dynamics of information propagation. Compared with the previous models, ours is more realistic and is distinguished by two key factors that had been neglected before: 1) the proposed model does not purely rely on epidemic theory but rather binds it with geometrical and spatial constraints of real-world sensor networks and 2) it extends to also model the spread dynamics of conflicting information (e.g., a worm and its patch). We do extensive simulations to show the accuracy of our model and compare it with the previous ones. The findings show the common intuition that the infection source is the best location to start patching from, which is not necessarily right. We show that this depends on many factors, including the time it takes for the patch to be developed, worm/patch characteristics as well as the shape of the network.

Following targets for mobile tracking in wireless sensor networks

Traditional tracking solutions in wireless sensor networks based on fixed sensors have several critical problems. First, due to the mobility of targets, a lot of sensors have to keep being active to track targets in all potential directions, which causes excessive energy consumption. Second, when there are holes in the deployment area, targets may fail to be detected when moving into holes. Third, when targets stay at certain positions for a long time, sensors surrounding them have to suffer heavier work pressure than do others, which leads to a bottleneck for the entire network. To solve these problems, a few mobile sensors are introduced to follow targets directly for tracking because the energy capacity of mobile sensors is less constrained and they can detect targets closely with high tracking quality. Based on a realistic detection model, a solution of scheduling mobile sensors and fixed sensors for target tracking is proposed. Moreover, the movement path of mobile sensors has a provable performance bound compared to the optimal solution. Results of extensive simulations show that mobile sensors can improve tracking quality even if holes exist in the area and can reduce energy consumption of sensors effectively.

An adaptive elliptical anomaly detection model for wireless sensor networks

Wireless Sensor Networks (WSNs) provide a low cost option for monitoring different environments such as farms, forests and water and electricity networks. However, the restricted energy resources of the network impede the collection of raw monitoring data from all the nodes to a single location for analysis. This has stimulated research into efficient anomaly detection techniques to extract information about unusual events such as malicious attacks or faulty sensors at each node. Many previous anomaly detection methods have relied on centralized processing of measurement data, which is highly communication intensive. In this paper, we present an efficient algorithm to detect anomalies in a decentralized manner. In particular, we propose a novel adaptive model for anomaly detection, as well as a robust method for modeling normal behavior. Our evaluation results on both real-life and simulated data sets demonstrate the accuracy of our approach compared to existing methods.