On Lifetime Maximization and Fault Tolerance Measurement in Wireless Ad Hoc and Sensor Networks

In this dissertation we study two important issues in wireless ad hoc and sensor networks: lifetime maximization and fault tolerance. The first part investigates how to maximally extend the lifetime of randomly deployed wireless sensor networks under limited resource constraint, and the second part focuses on how to measure the fault tolerance and attack resilience of wireless ad hoc networks. We take the approach of adaptive traffic distribution and power control to maximize the lifetime of randomly deployed wireless sensor networks. After abstracting the network into multiple layers, we model the lifetime maximization problem as a linear program. We study both scenarios where receiving/processing power consumption is ignored and receiving/processing is included. In both cases, we have a similar observation: for each packet to be sent, the sender should either transmit it using the transmission range with the highest energy efficiency per bit per meter, or transmit it directly to the sink. We then prove it is true in general. Finally, we propose a fully distributed algorithm to adaptively split traffic and adjust transmission power. Extensive simulation studies demonstrate that the network lifetime can be dramatically extended by applying the proposed approach in various scenarios. Besides studying the lifetime extension problem for fully deployed wireless sensor networks, we also investigate how to extend the network lifetime via joint relay node deployment and adaptive traffic distribution. We formulate this problem as a mixed-integer nonlinear-program problem, which is NP-hard in general. We then propose a greedy heuristic to attack it. Both numerical and simulation results show that significant network lifetime extension can be achieved. In the second part of this dissertation, we investigate how to measure the fault tolerance and attack resilience for randomly deployed wireless ad hoc networks. We first propose two new metrics to measure the average case of network service quality: average pairwise connectivity and pairwise connected ratio. We then propose the fault tolerance and attack resilience metric: alpha-p-resilience, where a network is alpha-p-resilient if at least alpha portion of nodes pairs remain connected as long as no more than p fraction of nodes is removed from the network

Concepts and evolution of research in the field of wireless sensor networks

The field of Wireless Sensor Networks (WSNs) is experiencing a resurgence of interest and a continuous evolution in the scientific and industrial community. The use of this particular type of ad hoc network is becoming increasingly important in many contexts, regardless of geographical position and so, according to a set of possible application. WSNs offer interesting low cost and easily deployable solutions to perform a remote real time monitoring, target tracking and recognition of physical phenomenon. The uses of these sensors organized into a network continue to reveal a set of research questions according to particularities target applications. Despite difficulties introduced by sensor resources constraints, research contributions in this field are growing day by day. In this paper, we present a comprehensive review of most recent literature of WSNs and outline open research issues in this field

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Probabilistic approaches to the design of wireless ad hoc and sensor networks

The emerging wireless technologies has made ubiquitous wireless access a reality and enabled wireless systems to support a large variety of applications. Since the wireless self-configuring networks do not require infrastructure and promise greater flexibility and better coverage, wireless ad hoc and sensor networks have been under intensive research. It is believed that wireless ad hoc and sensor networks can become as important as the Internet. Just as the Internet allows access to digital information anywhere, ad hoc and sensor networks will provide remote interaction with the physical world. Dynamics of the object distribution is one of the most important features of the wireless ad hoc and sensor networks. This dissertation deals with several interesting estimation and optimization problems on the dynamical features of ad hoc and sensor networks. Many demands in application, such as reliability, power efficiency and sensor deployment, of wireless ad hoc and sensor network can be improved by mobility estimation and/or prediction. In this dissertation, we study several random mobility models, present a mobility prediction methodology, which relies on the analysis of the moving patterns of the mobile objects. Through estimating the future movement of objects and analyzing the tradeoff between the estimation cost and the quality of reliability, the optimization of tracking interval for sensor networks is presented. Based on the observation on the location and movement of objects, an optimal sensor placement algorithm is proposed by adaptively learn the dynamical object distribution. Moreover, dynamical boundary of mass objects monitored in a sensor network can be estimated based on the unsupervised learning of the distribution density of objects. In order to provide an accurate estimation of mobile objects, we first study several popular mobility models. Based on these models, we present some mobility prediction algorithms accordingly, which are capable of predicting the moving trajectory of objects in the future. In wireless self-configuring networks, an accurate estimation algorithm allows for improving the link reliability, power efficiency, reducing the traffic delay and optimizing the sensor deployment. The effects of estimation accuracy on the reliability and the power consumption have been studied and analyzed. A new methodology is proposed to optimize the reliability and power efficiency by balancing the trade-off between the quality of performance and estimation cost. By estimating and predicting the mass objects\u27 location and movement, the proposed sensor placement algorithm demonstrates a siguificant improvement on the detection of mass objects with nearmaximal detection accuracy. Quantitative analysis on the effects of mobility estimation and prediction on the accuracy of detection by sensor networks can be conducted with recursive EM algorithms. The future work includes the deployment of the proposed concepts and algorithms into real-world ad hoc and sensor networks

Distributed Intermittent Fault Diagnosis in Wireless Sensor Network Using Likelihood Ratio Test

In current days, sensor nodes are deployed in hostile environments for various military and commercial applications. Sensor nodes are becoming faulty and having adverse effects in the network if they are not diagnosed and inform the fault status to other nodes. Fault diagnosis is difficult when the nodes behave faulty some times and provide good data at other times. The intermittent disturbances may be random or kind of spikes either in regular or irregular intervals. In literature, the fault diagnosis algorithms are based on statistical methods using repeated testing or machine learning. To avoid more complex and time consuming repeated test processes and computationally complex machine learning methods, we proposed a one shot likelihood ratio test (LRT) here to determine the fault status of the sensor node. The proposed method measures the statistics of the received data over a certain period of time and then compares the likelihood ratio with the threshold value associated with a certain tolerance limit. The simulation results using a real time data set shows that the new method provides better detection accuracy (DA) with minimum false positive rate (FPR) and false alarm rate (FAR) over the modified three sigma test. LRT based hybrid fault diagnosis method detecting the fault status of a sensor node in wireless sensor network (WSN) for real time measured data with 100% DA, 0% FAR and 0% FPR if the probability of the data from faulty node exceeds 25%

Attacks on Geographic Routing Protocols for Wireless Sensor Network

With the increase in the military and several other applications of Wireless Sensor Network, provisions must be made for secure transmission of sensitive information throughout the network. Most of the routing protocols proposed for ad-hoc networks and sensor networks are not designed with security as a goal. Hence, many routing protocols are vulnerable to an attack by an adversary who can disrupt the network or harness valuable information from the network. Routing Protocols for wireless sensor networks are classified into three types depending on their network structure as Flat routing protocols, Hierarchical routing protocol and Geographic routing protocols. Large number of nodes in a wireless sensor network , limited battery power and their data centric nature make routing in wireless sensor network a challenging problem. We mainly concentrate on location-based or geographic routing protocol like Greedy Perimeter Stateless Routing Protocol. Sybil attack and Selective forwarding attack are the two attacks feasible in GPSR. These attacks are implemented in GPSR and their losses caused to the network are analysed