Zahra Taghikhaki

Use of wireless sensor networks for distributed event detection in disaster management applications

Recently, wireless sensor networks (WSNs) have become mature enough to go beyond being simple fine-grained continuous monitoring platforms and have become one of the enabling technologies for early-warning disaster systems. Event detection functionality of WSNs can be of great help and importance for (near) real-time detection of, for example, meteorological natural hazards and wild and residential fires. From the data-mining perspective, many real world events exhibit specific patterns, which can be detected by applying machine learning (ML) techniques. In this paper, we introduce ML techniques for distributed event detection in WSNs and evaluate their performance and applicability for early detection of disasters, specifically residential fires. To this end, we present a distributed event detection approach incorporating a novel reputation-based voting and the decision tree and evaluate its performance in terms of detection accuracy and time complexity.

A trust-based probabilistic coverage algorithm for wireless sensor networks

Sensing coverage is a fundamental issue for many applications in wireless sensor networks. Due to sensors resource limitations, inherent uncertainties associated with their measurements, and the harsh and dynamic environment in which they are deployed, having a QoS-aware coverage scheme is a must. In this paper, we propose a Trust-based Probabilistic Coverage algorithm, which leverages the trust concept to tackle the uncertainties introduced by the nodes and the environment, in which they operate. We formulate this problem as an Integer Linear Programming (ILP) problem, which is able to always guarantee the required QoS despite uncertainties introduced by node and/or environment. In consideration of the limitation of ILP, we also put forward a greedy heuristic algorithm to achieve almost the same ILP results without suffering from complexities imposed by ILP. We examine our heuristic with different input parameters and compare it with the ILP approach. Simulation results are presented to verify our approaches.

Energy-efficient Trust-based aggregation in wireless sensor networks

Wireless sensor networks (WSNs) are often deployed in unattended and noise-prone environments and suffer from energy constraints that limit the quality and quantity of data transmission. Every decision made based on the low quality and low quantity data may have drastic consequence. Therefore, ensuring high quality of sensor data and preserving energy resources of the sensor nodes go hand in hand. In this paper, we propose ETA that is an Energy-efficient Trust based data aggregation aims to achieve reliable and energy-efficient data transmission and aggregation. We use the concept of functional reputation and trust as a means to reach reliability. Functional reputation is used to select nodes that best satisfy the criteria to be an aggregator on the basis of the quality of the node. To find out the best path from every sensor node to the aggregator we take into account the link availability and residual energy of the nodes over the path. Simulation results show that even though ETA introduces some delays, overall it outperforms the other approaches in terms of reliability and lifetime.

Sensor Fusion-based Activity Recognition for Parkinson Patients

Parkinson disease (PD) is a slow destructive disorder of the central nervous system in which dopamine, i.e., catecholamine neurotransmitter in the central nervous system is lost. PD hurts patients’ movement and speech ability. Sometimes, it can also affect patients’ mood, behavior, and thinking ability. Falling down is a common problem in PD patients and on time fall detection is important to assist PD patients and prevent them from being injured. To this end, being able to correctly distinguish various activities, e.g. walking, sitting, standing still, is a must. To monitor activities and moving patterns of PD patients, a wireless body sensor network (BSN) may prove to be useful. By attaching various wireless sensor nodes on the body of PD patients or integrating them into their shoes or cloths, their activities and physiological conditions can be checked regularly and an alarm can be generated in case of emergency or need for additional assistances. A wireless body sensor network consists of a number of wireless sensor nodes that cooperatively monitor physical (e.g. motion) and physiological (e.g. heart rate) conditions of a person. In addition to sensors, each sensor node is typically equipped with a radio transceiver or other wireless communication devices, a small microcontroller as processing unit, and an energy source in a form of a battery. Sensor nodes may vary in size and type of sensors they are equipped with. Size and cost constraints on sensor nodes cause limitations on their resources in terms of energy, memory, and computational processing. Figure 1 shows an example of a body sensor network. Previous studies for activity recognition of PD patients mostly use accelerometer and occasionally gyroscope sensors attached to various parts of patients’ body (JJ., HA. et al. 1991; Aminian, Robert et al. 1999; JI., AA. et al. 2001; JI., V. et al. 2004; N., T. et al. 2004; White, Wagenaar et al. 2006; Moorea, MacDougalla et al. 2007; Salarian, Russmann et al. 2007). One of the main criticisms on the previous studies is that they use centralized techniques which not only require expensive equipments to monitor physiological conditions and activities of patients [e.g. Vitaport 3 (White, Wagenaar et al. 2006)] but also introduce delays in the detection process. Also due to having a single point of failure they are more prone to failures and crashes. In contrary, we propose a fusion-based distributed algorithm which can be easily implemented on resource constrained wireless sensor nodes and detect and distinguish activities in (near) real-time. Our approach offers three main advantages: (i) distributed processing and reasoning which decreases the data processing

Survivability modeling of wireless sensor networks

Network survivability is one of the main design challenges in wireless sensor networks (WSN). Survivability is the ability of the network to provide essential services in the presence of attacks and/or failures, and recover full service in a timely manner. To the best knowledge of us, survivability for WSN has never been studied considering failure effects on network. We consider the network survivability as a composite measure consisting of both network failure duration and failure impact on the network. In this paper we model network survivability in steady state. In this state, network is affected by predicted failures. This modeling is based on network connectivity and coverage, when an area loses its connectivity; it is connected to the main node by using the covering nearby areas.

A trust-based distributed data fault detection algorithm for wireless sensor networks

Fault detection is a difficult and complex task in WSN because of there are many factors that influence data and could cause faults. Large-scale sensor networks impose energy and communication constraints, thus it is difficult to collect data from each individual sensor node and process it at the sink to detect faulty sensors. The proposed approach saves energy and improves network lifetime by detecting data faults locally in cluster head and therefore reducing the number of transmissions required to convey relevant information to the sink. This paper presents a novel approach for detecting sensors which produce faulty data in a distributed way as well as identifying the type of data faults using trust concepts to gain a high degree of confidence. We validate our method with simulations results.

A reliable and energy-efficient chain-cluster based routing protocol for Wireless Sensor Networks

Reliable and energy-efficient data dissemination is an important challenge particularly in multi-hop Wireless Sensor Networks (WSNs). Although clustering is considered as one of the promising techniques for energy aware data dissemination, majority of research in this area assume a reliable network, in which no packet is lost due to low link quality. In this paper we propose REC+, a Reliable and Energy-efficient Chain-cluster based routing protocol, which aims to achieve the maximum reliability in a multi-hop network by finding the best place for the Cluster Head (CH) and the proper shape/size of the clusters without the need of using any error controlling approaches that can be quite expensive in terms of computation and communication overhead. Most importantly, REC+ relaxes some strong assumptions that other cluster-based routing algorithms rely on, which make them inapplicable for real WSNs. To the best of our knowledge, REC+ is the first cluster based routing algorithm that considers energy efficiency, transmission reliability and intra-cluster delay all together to construct clusters and select proper CHs in WSNs. In the simulation, we show superiority of our approach over three others in terms of the product of energy consumption and delay.

An Error Control Scheme for Delay Constrained Data Communication in a Chain-Based Wireless Sensor Network

Delay sensitive applications of Wireless Sensor Networks (WSNs) demand timely data delivery for fast identification of out-of-ordinary situations and fast and reliable delivery of notification and warning messages. Due to unreliable nature of WSNs, achieving real-time guarantees and providing reliable data are quite challenging. Reliable data dissemination is traditionally performed by applying error control protocols which are not suitable for time critical applications deployed in a packet burst loss WSN. In this paper, we propose READ+ which is an enhanced version of READ, i.e., a real time and energy efficient error controlling scheme to ensure reliability for delay constrained aggregated data over a bursty channel in a chain-based WSN. In READ+ link qualities are updated either locally by sensor nodes or globally by the Base Station (BS). The simulation results show READ+ performs better considering both hit ratio and energy efficiency compared with the XOR based Forward Error Correction (FEC) and the stop-and-wait (S-W) ARQ specially when duty cycling is low and when either average packet loss is low or packet TTL is short.

Survivability Analysis of Wireless Sensor Network with Transient Faults

To the best knowledge of us, survivability for WSN has never been studied considering failure affects on network. We perceive the network survivability as a composite measure consisting of both network failure duration and failure impact on the network. In this paper we will study network survivability in unstably state: in this state, network is affected by the failures that occur temporarily and instantly also may occur several times. In other words, the main characteristics of these failures are their frequency and being temporary. In this paper we will propose a survivability model for network in unstable state that is based on network availability. This availability model, presents frequent availability of a route. During this paper, first we acquire an availability model for network in unstable state that shows frequent availability of a node. We also use Markov model for nodes to show their transmission state according to availability model. Then we use a computational method for proving our model.

An Efficient Algorithm to Detect Faulty Reading in Wireless Sensor Network Using the Concept of Reputation

This paper presents a novel approach for detecting faulty sensors as well as identifying the type of faults with a high degree of confidence using reputation concepts and introducing special nodes as beacon nodes to the network and with very low false alarm rate. Our algorithm is purely localized and thus scales well to large sensor networks as well as being efficient with respect to the amount of generated traffic and resource usage. The computational overhead for a sensor is low, since only simple operations are involved in each sensor node and main tasks are done in beacon nodes which have a lot of resources and number of them is low. The complexity of the algorithm is very low and the probability of correct diagnosis is very high even in the existence of large fault sets. Simulation results show the algorithm can clearly identify the faulty sensors with high degree of trust and low communications for sensors.