Hüseyin Akcan

GPS-Free node localization in mobile wireless sensor networks

An important problem in mobile ad-hoc wireless sensor networks is the localization of individual nodes, i.e., each nodes awareness of its position relative to the network. In this paper, we introduce a variant of this problem (directional localization) where each node must be aware of both its position and orientation relative to the network. This variant is especially relevant for the applications in which mobile nodes in a sensor network are required to move in a collaborative manner. Using global positioning systems for localization in large scale sensor networks is not cost effective and may be impractical in enclosed spaces. On the other hand, a set of pre-existing anchors with globally known positions may not always be available. To address these issues, in this work we propose an algorithm for directional node localization based on relative motion of neighboring nodes in an ad-hoc sensor network without an infrastructure of global positioning systems (GPS), anchor points, or even mobile seeds with known locations. Through simulation studies, we demonstrate that our algorithm scales well for large numbers of nodes and provides convergent localization over time, even with errors introduced by motion actuators and distance measurements. Furthermore, based on our localization algorithm, we introduce mechanisms to preserve network formation during directed mobility in mobile sensor networks. Our simulations confirm that, in a number of realistic scenarios, our algorithm provides for a mobile sensor network that is stable over time irrespective of speed, while using only constant storage per neighbor.

GPS-free directional localization via dual wireless radios

Location discovery, especially in mobile environments, has recently become the key component of many applications. Accurate location discovery, particularly in safety critical applications using autonomous robots or unmanned vehicles, however, is still an open problem. Existing popular methods either heavily rely on the use of global positioning systems (GPS) which do not readily lend themselves for use for the majority of applications where precision is of primary concern or are not suitable for ad-hoc deployments. In this paper, we propose a novel directional localization algorithm, called dual wireless radio localization (DWRL), which performs accurate node localizations in the plane using only distances between nodes, without the use of a GPS or nodes with known positions (anchors). The main novelty of DWRL is the use of an additional radio per node to support directional localization in static networks. To the best of our knowledge, this is the first time dual radios are employed in a localization setting. Existence of the dual radios on board enables DWRL algorithm to perform directional localization, which is not possible with existing single radio systems in static networks. We present the practical and theoretical benefits of the use of an additional radio per node in detail, test our algorithm under excessive synthetic and real-world noise scenarios, and show that DWRL algorithm is robust enough to perform directional localization even in high noise environments.

On the Complexity of Trilateration with Noisy Range Measurements

Recent developments, especially in wireless and mobile networks, have enabled the use of location based services in many application areas. Accurate location discovery, however, is still an open problem. A widely used and practical localization method is trilateration. However, trilateration works best when exact range measurements are available, which is not apparently the case in real-world due to device errors or environmental noise. In this paper, localization through trilateration when the distance measurements are imprecise, is shown to be NP-complete. Moreover, we also prove that no matter how small the ranging errors get, the problem is still intractable. This result alone justifies the need for new models for localization which are robust enough to operate even in noisy environments.

A new deterministic data aggregation method for wireless sensor networks

The processing capabilities of wireless sensor nodes enable to aggregate redundant data to limit total data flow over the network. The main property of a good aggregation algorithm is to extract the most representative data by using minimum resources. From this point of view, sampling is a promising aggregation method, that acts as surrogate for the whole data, and once extracted can be used to answer multiple kinds of queries (such as AVG, MEDIAN, SUM, COUNT, etc.), at no extra cost to the sensor network. Additionally, sampling also preserves correlations between attributes of multi-dimensional data, which is quite valuable for further data mining. In this paper, we propose a novel, distributed, weighted sampling algorithm to sample sensor network data and compare to an existing random sampling algorithm, which is the only algorithm to work in this kind of setting. We perform popular queries to evaluate our algorithm on a real world data set, which covers climate data in the US for the past 100 years. During testing, we focus on issues such as sample quality, network longevity, energy and communication costs.

Deterministic Data Reduction in Sensor Networks

The processing capabilities of wireless sensor nodes enable to aggregate redundant data to limit total data flow over the network. The main property of a good aggregation algorithm is to extract the most representative data by using minimum resources. From this point of view, sampling is a promising aggregation method, that acts as surrogate for the whole data, and once extracted can be used to answer multiple kinds of queries (such as AVG, MEDIAN, SUM, COUNT, etc.), at no extra cost. Additionally, sampling also preserves the correlation info within multi-dimensional data, which is quite valuable for further data mining. In this paper, we propose a novel, distributed, weighted sampling algorithm to sample sensor network data and compare to an existing random sampling algorithm, which to the best of our knowledge is the only algorithm to work in this kind of setting

Deterministic algorithms for sampling count data

Processing and extracting meaningful knowledge from count data is an important problem in data mining. The volume of data is increasing dramatically as the data is generated by day-to-day activities such as market basket data, web clickstream data or network data. Most mining and analysis algorithms require multiple passes over the data, which requires extreme amounts of time. One solution to save time would be to use samples, since sampling is a good surrogate for the data and the same sample can be used to answer many kinds of queries. In this paper, we propose two deterministic sampling algorithms, Biased-L2 and DRS. Both produce samples vastly superior to the previous deterministic and random algorithms, both in sample quality and accuracy. Our algorithms also improve on the run-time and memory footprint of the existing deterministic algorithms. The new algorithms can be used to sample from a relational database as well as data streams, with the ability to examine each transaction only once, and maintain the sample on-the-fly in a streaming fashion. We further show how to engineer one of our algorithms (DRS) to adapt and recover from changes to the underlying data distribution, or sample size. We evaluate our algorithms on three different synthetic datasets, as well as on real-world clickstream data, and demonstrate the improvements over previous art.

Managing cohort movement of mobile sensors via GPS-free and compass-free node localization

A critical problem in mobile ad hoc wireless sensor networks is each nodes awareness of its position relative to the network. This problem is known as localization. In this paper, we introduce a variant of this problem, directional localization, where each node must be aware of both its position and orientation relative to its neighbors. Directional localization is relevant for applications that require uniform area coverage and coherent movement. Using global positioning systems for localization in large scale sensor networks may be impractical in enclosed spaces, and might not be cost effective. In addition, a set of pre-existing anchors with globally known positions may not always be available. In this context, we propose two distributed algorithms based on directional localization that facilitate the collaborative movement of nodes in a sensor network without the need for global positioning systems, seed nodes or a pre-existing infrastructure such as anchors with known positions. Our first algorithm, GPS-free Directed Localization (GDL) assumes the availability of a simple digital compass on each sensor node. We relax this requirement in our second algorithm termed GPS- and Compass-free Directed Localization (GCDL). Through experimentation, we demonstrate that our algorithms scale well for large numbers of nodes and provide convergent localization over time, despite errors introduced by motion actuators and distance measurements. In addition, we introduce mechanisms to preserve swarm formation during directed sensor network mobility. Our simulations confirm that, in a number of realistic scenarios, our algorithms provide for a mobile sensor network that preserves its formation over time, irrespective of speed and distance traveled. We also present our method to organize the sensor nodes in a polygonal geometric shape of our choice even in noisy environments, and investigate the possible uses of this approach in search-and-rescue type of missions.

Alleviating the topology mismatch problem in distributed overlay networks

The topology mismatch problem of P2P architectures is systematically defined.We aggregate research tackling the topology mismatch problem in P2P systems.Both unstructured and structured overlay paradigms are surveyed.We provide tabular data of salient features of all surveyed approaches.Trending and temporal characteristics of algorithms are presented. Peer-to-peer (P2P) systems have enjoyed immense attention and have been widely deployed on the Internet for well over a decade. They are often implemented via an overlay network abstraction atop the Internets best-effort IP infrastructure. P2P systems support a plethora of desirable features to distributed applications including anonymity, high availability, robustness, load balancing, quality of service and scalability to name just a few. Unfortunately, inherent weaknesses of early deployments of P2P systems, prevented applications from leveraging the full potential of the paradigm. One major weakness, identified early on, is the topology mismatch problem between the overlay network and the underlying IP topology. This mismatch can impose an extraordinary amount of unnecessary stress on network resources and can adversely affect both the scalability and efficiency of the operating applications. In this paper, we survey over a decades worth of research efforts aimed at alleviating the topology mismatch problem in both structured and unstructured P2P systems. We provide a fine-grained categorization of the suggested solutions by discussing their novelty, advantages and weaknesses. Finally, we offer an analysis as well as pictorial comparisons of the reviewed approaches since we aim to offer a comprehensive reference for developers, system architects and researchers in the field.

Reducing the number of flips in trilateration with noisy range measurements

Many applications in wireless networks depend on accurate localization services to operate properly. Trilateration is a widely used range-based localization method that can operate in polynomial time, given that the distance measurements are precise. However in real-world, range measurements tend to have errors due to internal and external factors. Flip ambiguities that occur during trilateration as a consequence of imprecise range measurements turn localization via trilateration into an intractable problem. In this paper, we analyze ip ambiguities due to range measurement errors and propose a heuristic solution that tries to minimize the number of ips in trilateration even in highly noisy environments. We simulate our algorithms under various noise scenarios and observe that the use of our heuristic based solution effectively decreases the number of ips in trilateration and increases the accuracy of the localization.

On the complexity of energy efficient pairwise calibration in embedded sensors

Technological advances in nanotechnology enabled the use of microelectromechanical systems (MEMS) in various application areas. With the integration of various sensor devices into MEMS, autonomously calibrating these sensors become a major research problem. When performing calibration on real-world embedded sensor network deployments, random errors due to internal and external factors alter the calibration parameters and eventually effect the calibration quality in a negative way. Therefore, during autonomous calibration, calibration paths which has low cost and low error values are preferable. To tackle the calibration problem on embedded wireless sensor networks, we present an energy efficient and minimum error calibration model, and also prove that due to random errors the problem turns into an NP-complete problem. To the best of our knowledge this is the first time a formal proof is presented on the complexity of an iterative calibration based problem when random errors are present in the measurements. We also conducted heuristic tests using genetic algorithm to solve the optimization version of the problem, on various graphs. The NP-completeness result also reveals that more research is needed to examine the complexity of calibration in a more general framework in real-world sensor network deployments.