Sanjay K. Jha

The holes problem in wireless sensor networks: a survey

Several anomalies can occur in wireless sensor networks that impair their desired functionalities i.e., sensing and communication. Different kinds of holes can form in such networks creating geographically correlated problem areas such as coverage holes, routing holes, jamming holes, sink/black holes and worm holes, etc. We detail in this paper different types of holes, discuss their characteristics and study their effects on successful working of a sensor network. We present state-of-the-art in research for addressing the holes related problems in wireless sensor networks and discuss the relative strengths and short-comings of the proposed solutions for combating different kinds of holes. We conclude by highlighting future research directions.

Node localization using mobile robots in delay-tolerant sensor networks

We present a novel scheme for node localization in a delay-tolerant sensor network (DTN). In a DTN, sensor devices are often organized in network clusters that may be mutually disconnected. Some mobile robots may be used to collect data from the network clusters. The key idea in our scheme is to use this robot to perform location estimation for the sensor nodes it passes based on the signal strength of the radio messages received from them. Thus, we eliminate the processing constraints of static sensor nodes and the need for static reference beacons. Our mathematical contribution is the use of a robust extended Kalman filter (REKF)-based state estimator to solve the localization. Compared to the standard extended Kalman filter, REKF is computationally efficient and also more robust. Finally, we have implemented our localization scheme on a hybrid sensor network test bed and show that it can achieve node localization accuracy within 1 m in a large indoor setting.

The design and evaluation of a hybrid sensor network for Cane-Toad monitoring

This paper investigates a wireless, acoustic sensor network application --- monitoring amphibian populations in the monsoonal woodlands of northern Australia. Our goal is to use automatic recognition of animal vocalizations to census the populations of native frogs and the invasive introduced species, the Cane Toad (see Fig. 1). This is a challenging application because it requires high frequency acoustic sampling, complex signal processing and wide area sensing coverage.We set up two prototypes of wireless sensor networks that recognize vocalizations of up to 9 frog species found in northern Australia. Our first prototype is simple and consists of only resource-rich Stargate devices. Our second prototype is more complex and consists of a hybrid mixture of Stargates and inexpensive, resource-poor Mica2 devices operating in concert. In the hybrid system, the Mica2s are used to collect acoustic samples, and expand the sensor network coverage. The Stargates are used for resource-intensive tasks such as Fast Fourier Transforms (FFTs) and machine learning.The hybrid system incorporates three algorithms designed to account for the sampling, processing and communication bottlenecks of the Mica2s (i) high frequency sampling, (ii) compression and noise reduction, to reduce data transmission by up to 90%, and (iii) sampling scheduling, which exploits the sensor network redundancy to increase the effective sample processing rate.We evaluate the performance of both systems over a range of scenarios, and demonstrate that the feasibility and benefits of a hybrid systems approach justify the additional system complexity.

Probabilistic coverage in wireless sensor networks

The sensing capabilities of networked sensors are affected by environmental factors in real deployment and it is imperative to have practical considerations at the design stage in order to anticipate this sensing behavior. We investigate the coverage issues in wireless sensor networks based on probabilistic coverage and propose a distributed probabilistic coverage algorithm (PCA) to evaluate the degree of confidence in detection probability provided by a randomly deployed sensor network. The probabilistic approach is a deviation from the idealistic assumption of uniform circular disc for sensing coverage used in the binary detection model. Simulation results show that area coverage calculated by using PCA is more accurate than the idealistic binary detection model

An adaptive mobility-aware MAC protocol for sensor networks (MS-MAC)

Most of the MAC protocols proposed for wireless sensor networks assume sensors to be stationary after deployment. This usually provides very bad network performance in scenarios involving mobile sensors. Handling mobility in wireless sensor networks in an energy-efficient way is a new challenge. Techniques developed for other mobile networks, such as mobile phone or mobile ad hoc networks, cannot be applicable, as in these networks, energy is not a very critical resource. The paper presents a new adaptive mobility-aware MAC protocol for sensor networks (MS-MAC). The protocol uses any change in received signal level as an indication of mobility and, when necessary, triggers the mobility handling mechanism. In this way, the new mobility-aware MAC protocol can work in a very energy-efficient way when the network is stationary, whereas it can maintain some level of network performance when there are mobile sensors.

Design and evaluation of a hybrid sensor network for cane toad monitoring

This paper investigates a wireless, acoustic sensor network application-monitoring amphibian populations in the monsoonal woodlands of northern Australia. Our goal is to use automatic recognition of animal vocalizations to census the populations of native frogs and the invasive introduced species, the cane toad. This is a challenging application because it requires high frequency acoustic sampling, complex signal processing and wide area sensing coverage. We set up two prototypes of wireless sensor networks that recognize vocalizations of up to 9 frog species found in northern Australia. Our first prototype is simple and consists of only resource-rich Stargate devices. Our second prototype is more complex and consists of a hybrid mixture of Stargates and inexpensive, resource-poor Mica2 devices operating in concert. In the hybrid system, the Mica2s are used to collect acoustic samples, and expand the sensor network coverage. The Stargates are used for resource-intensive tasks such as fast Fourier transforms (FFTs) and machine learning. The hybrid system incorporates three algorithms designed to account for the sampling, processing and communication bottlenecks of the Mica2s (i) high frequency sampling, (ii) compression and noise reduction, to reduce data transmission by up to 90%, and (iii) sampling scheduling, which exploits the sensor network redundancy to increase the effective sample processing rate. We evaluate the performance of both systems over a range of scenarios, and demonstrate that the feasibility and benefits of a hybrid systems approach justify the additional system complexity.

A communication paradigm for hybrid sensor/actuator networks

This paper investigates an anycast communication service for a hybrid sensor/actuator network, consisting of both resource-rich and resource-impoverished devices. The key idea is to exploit the capabilities of resource-rich devices (called micro-servers) to reduce the communication burden on smaller, energy, bandwidth and memory constrained sensor nodes. The goal is to deliver sensor data to the nearest micro-server, which can (i) store it (ii) forward it to other micro-servers using out-of-band communication or (iii) perform the desired actuation. We propose and evaluate a reverse tree-based anycast mechanism tailored to deal with the unique event dynamics in sensor networks. Our approach is to construct an anycast tree rooted at each potential event source, which micro-servers can dynamically join and leave. Our anycast mechanism is self-organizing, distributed, robust, scalable, routing-protocol independent and incurs very little overhead. Simulations using Network Simulator (ns-2) show that: our anycast mechanism when added to Directed Diffusion can reduce the network’s energy consumption by more than 50%; can reduce both the mean end-to-end latency of the transmission and the mean number of transmissions by more than 50%; achieves 99% data delivery rate for low and moderate micro-server mobility rate; and handles network dynamics reasonably well.

Location estimation and trajectory prediction for cellular networks with mobile base stations

This paper provides mobility estimation and prediction for a variant of the GSM network that resembles an ad hoc wireless mobile network in which base stations and users are both mobile. We propose using a Robust Extended Kalman Filter (REKF) to derive an estimate of the mobile users next mobile base station from the users location, heading, and altitude, to improve connection reliability and bandwidth efficiency of the underlying system. Our analysis demonstrates that our algorithm can successfully track the mobile users with less system complexity, as it requires measurements from only one or two closest mobile base stations. Further, the technique is robust against system uncertainties caused by the inherent deterministic nature of the mobility model. Through simulation, we show the accuracy of our prediction algorithm and the simplicity of its implementation.

Secure Data Aggregation Technique for Wireless Sensor Networks in the Presence of Collusion Attacks

Due to limited computational power and energy resources, aggregation of data from multiple sensor nodes done at the aggregating node is usually accomplished by simple methods such as averaging. However such aggregation is known to be highly vulnerable to node compromising attacks. Since WSN are usually unattended and without tamper resistant hardware, they are highly susceptible to such attacks. Thus, ascertaining trustworthiness of data and reputation of sensor nodes is crucial for WSN. As the performance of very low power processors dramatically improves, future aggregator nodes will be capable of performing more sophisticated data aggregation algorithms, thus making WSN less vulnerable. Iterative filtering algorithms hold great promise for such a purpose. Such algorithms simultaneously aggregate data from multiple sources and provide trust assessment of these sources, usually in a form of corresponding weight factors assigned to data provided by each source. In this paper we demonstrate that several existing iterative filtering algorithms, while significantly more robust against collusion attacks than the simple averaging methods, are nevertheless susceptive to a novel sophisticated collusion attack we introduce. To address this security issue, we propose an improvement for iterative filtering techniques by providing an initial approximation for such algorithms which makes them not only collusion robust, but also more accurate and faster converging.

Mobility modelling and trajectory prediction for cellular networks with mobile base stations

This paper provides mobility estimation and prediction for a variant of GSM network which resembles an adhoc wireless mobile network where base stations and users are both mobile. We propose using Robust Extended Kalman Filter (REKF)as a location heading altitude estimator of mobile user for next node (mobile-base station)in order to improve the connection reliability and bandwidth efficiency of the underlying system. Through analysis we demonstrate that our algorithm can successfully track the mobile users with less system complexity as it requires either one or two closest mobile-basestation measurements. Further, the technique is robust against system uncertainties due to inherent deterministic nature in the mobility model. Through simulation, we show the accuracy and simplicity in implementation of our prediction algorithm.