Rami Tawil

Energy-efficient data aggregation and transfer in periodic sensor networks

Limited battery power and high transmission energy consumption in wireless sensor networks make in-network aggregation and prediction a challenging area for researchers. The most energy consumable operation is transmitting data by a sensor node, comparing it with the energy consumption of in-network computation which is negligible. The energy trade-off between communication and computation provides applications benefit when processing the data at the network side rather than simply transmitting sensor data. In this study, the authors consider a cluster-based technique with which data is sent periodically from sensor nodes to their appropriate cluster-heads (CH). The proposed technique manages energy efficiency in periodic sensor network and it consists of two phases: ‘aggregation phase and adaptation phase’. The aggregation phase is used to find similarities between data (measurements captured during a period p) in order to eliminate redundancy from raw data, thus reducing the amount of data-sets sent to the CH. The adaptation phase provides sensors the ability to identify duplicate data-sets captured among successive periods, using the sets-similarity joins functions. To evaluate the performance of the proposed technique, experiments on real sensor data have been conducted. Results show that the proposed technique is effective in term of energy consumption and quality of data.

K-means based clustering approach for data aggregation in periodic sensor networks.

In-network data aggregation becomes an important technique to achieve efficient data transmission in wireless sensor networks (WSN). Energy efficiency, data latency and data accuracy are the major key elements evaluating the performance of an in-network data aggregation technique. The trade-offs among them largely depends on the specific application. For instance, prefix frequency filtering (PFF) is a good recently example for an in-network data aggregation technique that optimizing energy consumption and data accuracy. The objective of PFF is to find similar data sets generated by neighboring nodes in order to reduce redundancy of the data over the network and thus to preserve the nodes energy. Unfortunately, this technique has a heavy computational load. In this paper, we propose an enhanced new version of the PFF technique called KPFF technique. In this new technique, we propose to integrate a K-means clustering algorithm on data before applying the PFF on the generated clusters. By this way we minimize the number of comparisons to find similar data sets and thus we decrease the data latency. Experiments on real sensors data show that our new technique can significantly reduce the computational time without affecting the data aggregation performance of the PFF technique.

A suffix-based enhanced technique for data aggregation in periodic sensor networks

Data aggregation in wireless sensor networks (WSN) has been proven as an effective technique for eliminating redundancy and forwarding only the extracted information from the raw data. Furthermore, by doing so data aggregation can often reduce the communication cost and extend the whole network lifetime. In this paper we study a new prefix-suffix filtering technique for data aggregation in periodic sensor networks (PSN). We investigate the problem of finding all pair of nodes generating similar data sets. We added a new suffix frequency filter technique to the existing prefix frequency filtering. Our goal is to integrate additional filtering technique in order to decrease the latency of the aggregation phase. Our simulation results show that our technique outperforms existing prefix filtering technique in reducing energy consumption.

A new software-based service provision approach for vehicular cloud

Vehicles belonging to a Vehicular Ad hoc NETwork (VANET) can form an ad hoc vehicular cloud in order to provide and to share services between the vehicles. These services may be divided into two categories: (1) Infrastructure as a Service (IaaS) describing physical resources shared as services such as data storage, sensor data, network and computing services; and (2) Software as a Service (SaaS) describing applications that can be run as services in the network. In this paper, a SaaS is an infotainment application requiring multiple infrastructural services such as sensor data. In this context, we propose a service provision mechanism well adapted for VANET. The mechanism aims to select convenient service provider vehicles taking into account the mobility of the vehicles. The mechanism is evaluated by a series of experimentations on OMNeT++ network simulator. The results show a high service delivery ratio representing a satisfactory result for the simulated case study.

Adaptive data collection approach based on sets similarity function for saving energy in periodic sensor networks

Disaster monitoring becomes a requirement for collecting and analysing data in order to offer a better disaster management situation. Periodic sensor networks PSNs are usually used in disaster monitoring and are characterised by the acquisition of sensor data from remote sensor nodes before being forwarded to the sink in a periodic basis. The major challenges in PSN are energy saving and collected data reduction in order to increase the sensor network lifetime and to ensure a long-time monitoring for disasters. In this paper, we propose an adaptive sampling approach for energy-efficient periodic data collection in sensor networks. Our proposed approach provides each sensor node the ability to identify redundancy between collected data over time, by using similarity functions, and allowing for sampling adaptive rate. Experiments on real sensors data show that our approach can be effectively used to conserve energy in the sensor network and to increase its lifetime, while still keeping a high quality of the collected data.

On the Topology Effects in Wireless Sensor Networks Based Prognostics and Health Management

In this work, we consider the usage of wireless sensor networks (WSN) to monitor an area of interest, in order to diagnose on real time its state. Each sensor node forwards information about relevant features towards the sink where the data is processed. Nevertheless, energy conservation is a key issue in the design of such networks and once a sensor exhausts its resources, it will be dropped from the network. This will lead to broken links and data loss. It is therefore important to keep the network running for as long as possible by preserving the energy held by the nodes. Indeed, saving the quality of service (QoS) of a wireless sensor network for a long period is very important in order to ensure accurate data. Then, the area diagnosing will be more accurate. From another side, packet transmission is the phase that consumes the highest amount of energy comparing to other activities in the network. Therefore, we can see that the network topology has an important impact on energy efficiency, and thus on data and diagnosis accuracies. In this paper, we study and compare four network topologies: distributed, hierarchical, centralized, and decentralized topology and show their impact on the resulting estimation of diagnostics. We have used six diagnostic algorithms, to evaluate both prognostic and health management with the variation of type of topology in WSN.