Samer Samarah

An Efficient Data Extraction Mechanism for Mining Association Rules from Wireless Sensor Networks

With the advances of wireless sensor networks and their ability to generate a large amount of data, data mining techniques to extract useful knowledge regarding the underlying network have recently received a great deal of attention. However, the stream nature of the data, the limited resources, and the distributed nature of sensor networks bring new challenges for the mining techniques that need to be address. In this paper, we introduce a new formulation for the association rules, a well known data mining technique, that is able to generate the time relations between sensor devices in a particular sensor network. This new formulation will allow traditional data mining algorithms proposed to solve the classical association rules mining problem to be applied on sensor based class of applications that generate and use sensor data. The generated rules will give a clear picture about the correlations between sensors in the network and can be used to make decisions about the network performance, or it can be used to predict the sources of future events. In order to prepare for the data needed in the mining process and to maximize the network lifetime, a distributed extraction methodology is introduced, in this distributed methodology sensors perform optimization based on local computation to decide whether it will participate in sending data or not. Experimental results have shown that the distributed extraction solution is able to reduce the number of exchanged messages and the data size by 50% compared to a direct transmission of the data.

Vehicular movement patterns: A prediction-based route discovery technique for VANETs

Applications for Vehicular Ad-hoc Networks (VANETs) have been surfacing every day as with any new network architecture. One of the many challenges that have been addressed is the use of probabilistic models and behavioral patterns to predict routes vehicles undertake in a certain geographical area. This challenge, once overcome, could prove to be very beneficial for problems such as vehicular traffic control. This paper proposes a predictive technique based on sequential patterns and two mechanisms used to prepare data for this technique, as well as some performance evaluation for these mechanisms to determine the most feasible choice in terms of communication overhead. From the experiments, it can be noticed that one of the data collecting schemes has proven to be more efficient when travelling at different speeds.

A bio-inspired coverage-aware scheduling scheme for wireless sensor networks

In the densely deployed wireless sensor networks, sensors are scheduled over time in order to maintain the coverage while saving energy of networks. In this article, we investigate the coverage-aware scheduling problem using genetic algorithms. Sensors are optimally scheduled in different time slots to maximize the overall coverage under the given k-cover requirement and lifetime of networks. A set of simulation is carried out. The simulation result shows that, using the optimal schedule generated by genetic algorithm, our algorithm can optimize the coverage performance of wireless sensor network in terms of overall coverage rate and number of active sensors.

Coverage-Based Sensor Association Rules for Wireless Vehicular Ad Hoc and Sensor Networks

Recently, Knowledge Discovery Process has proven to be a promising tool for extracting behavioral patterns regarding sensor nodes from wireless vehicular ad hoc and sensor networks. In this paper, we propose a new type of behavioral patterns, which we refer to as Coverage-based Rules, to discovers the correlation among the set of locations monitored by the network. Coverage- base Rules is an extension for a recent proposed behavioral patterns named as Sensor Association Rules. However, in contrast to Sensor Association Rules, Coverage-based Rules have been designed specifically for sensor networks that guarantee a k- coverage property for the area under monitoring. The major application of Coverage-based Rules is to predict the location of future events. This feature might prove to be quite useful in vehicular ad hoc and sensor network based applications. To report about the efficiency of our proposed scheme, an extensive set of simulation experiments have been conducted to compare the performance of the network during the data preparation process for Coverage-based and Sensor Association Rules schemes.

CHRONOLOGICAL TREE A COMPRESSED STRUCTURE FOR MINING BEHAVIORAL PATTERNS FROM WIRELESS SENSOR NETWORKS

Wireless Sensor Networks (WSNs) have proven their success in a variety of applications for monitoring physical and critical environments. However, the streaming nature, limited resources, and the u...

Knowledge discovery in Wireless Sensor Networks for Chronological Patterns

Wireless Sensor Networks (WSNs) have proven their success in a variety of applications for monitoring physical and critical environments. However, the streaming nature, limited resources, and the unreliability of wireless communication are among the factors that affect the Quality of Service (QoS) of WSNs. In this paper, we propose a data mining technique to extract behavioral patterns about the sensor nodes during their operation. The behavioral patterns, which we refer to as Chronological Patterns, can be thought of as tutorials that teach about the set of sensors that report on events within a defined time interval and the order in which the events were detected. Chronological Patterns can serve as a helpful tool for predicting behaviors in order to enhance the performance of the WSN and thus improve the overall QoS. The proposed technique consists of: a formal definition of the Chronological Patterns and a new representation structure, which we refer to as Chlorotical Tree (CT), that facilities the mining of these patterns. To report about the performance of the CT, several experiments have been conducted to evaluate the CT using different density factors.

Target-based Association Rules for point-of-coverage wireless sensor networks

Recently, Knowledge Discovery Process has proven to be a promising tool for extracting behavioral patterns, regarding sensor nodes, from wireless sensor networks. In this paper, we propose a new type of behavioral patterns, which we refer to as Target-based Association Rules, to discover the correlation among a set of targets monitored by a network at a border region. Target-base Rules is an extension to a recently proposed behavioral patterns named Sensor Association Rules. The major application of Target-based Rules is to predict the source of future events. To report about the performance of our proposed knowledge technique, an extensive set of simulation experiments have been conducted to measure the performance of the network during the process of preparing the data needed for generating Target-based Rules.

PLT- Positional Lexicographic Tree: A New Structure for Mining Frequent Itemsets

Association rules have proved their influence in different industrial fields, where their goal is to identify the relations existing among the events that are stored in large databases. However, in order to enumerate the association rules, there is a need to identify the frequent set of itemsets (i.e. those events that occur together in a sufficient number of transactions). In this paper, a new representation structure for the data stored in any transactional database is proposed. This structure, which we refer to as positional lexicographic tree (PLT), provides an efficient mechanism for subset checking based on a summary of the data extracted from the database. This makes PLT a promising tool for most of the existing data mining approaches. Moreover, our proposed PLT structure regulates the data in the database so that they can be applicable to compression and indexing techniques, which makes PLT suitable for supporting large databases. First, we introduce the PLT construction process, then highlight the different mining approaches that can be modulated to take advantage of PLT. We then present our algorithm and finally prove its correctness

Design and performance evaluation of QoS aware and location based service discovery protocol for vehicular networks

Recently, we have witnessed a growing interest in the study of service discovery in vehicular networks that guarantee QoS to service requesters. To the best of our knowledge, none of the existing service discovery protocols in Vehicular Networks have been provided in the literature that guarantee QoS to service requesters nd to the Vehicular Network. For efficient service discovery in vehicular networks, it is very important to provide users with services that suit better to their requests while balancing the load on service providers. Moreover, for service discovery protocol integrated with routing protocols, it is important to guarantee load balancing on routing paths between service providers and service requesters. In this paper, we present a QoS aware location based service discovery protocol for Vehicular Networks. Our protocol guarantees load balancing on service providers, and routing paths between service providers and service requesters. It permits also to choose service providers and routing paths between service providers and service requesters that satisfy some performance attributes specified by service requesters. We present our QoS aware protocol, report on its performance evaluation, and discuss our experimental results we have obtained using realistic scenarios.1

Efficient data gathering and position dissemination protocols for heterogeneous vehicle ad hoc and sensor networks

Data gathering and localization algorithms for vehicular ad hoc networks (VANets) are envisioned as key technologies for the near future. These technologies will pave the way for a number of potential applications that required precise positioning information that GPS systems are not able to provide. However, high mobility of vehicles in a VANet introduces frequent topology changes that negatively affect existing solutions and poses significant challenges to developing effective localization and data gathering mechanisms. In this paper, we propose a new data gathering and localization dissemination protocol that uses an existing time-space synchronization technique to efficiently collect data from heterogeneous networks composed of wireless sensor nodes and vehicles in a VANet. Localization information is disseminated from vehicles to sensor nodes, which can compute their positions without the need of energy-hungry GPS modules. The focus of this paper is to provide a mechanism that is able to simultaneously collect data from the nodes, relay data to interested nodes, and to disseminate localization information that will aid the nodes to estimate their positions.