Ali Jaber

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.

Adaptive distributed energy-saving data gathering technique for wireless sensor networks

Popularity of wireless sensor networks (WSNs) is increasing day a day where hundreds or thousands of applications are explored. In most of such applications, the need of gathering data periodically about the monitored environment beside the limited, generally irreplaceable, power sensor sources make energy conservation and big data gathering reduction two fundamental challenges in such networks. In this paper, we propose an Adaptive Distributed Data Gathering (ADiDaG) technique for saving energy in periodic WSN applications. ADiDaG works into rounds where each round consists of three phases: data gathering, sampling decision, and transmission. These phases respectively use Map reduce, longest common subsequence similarity and grouping approach in order to search data redundancy and adapt sensor sampling rate at each round. The performance of ADiDaG is evaluated based on both simulation and experimentations where the obtained results show significant energy savings and high accurate data gathering compared to existing approaches.

A Distance-Based Data Aggregation Technique for Periodic Sensor Networks

Monitoring phenomena and environments is an emergent and required field in our today systems and applications. Hence, wireless sensor networks (WSNs) have attracted considerable attention from the research community as an efficient way to explore various kinds of environments. Sensor networks applications can be useful in different domains (terrestrial, underwater, space exploration, etc.). However, one of the major constraints in such networks is the energy consumption that increases when data transmission increases. Consequently, optimizing data transmission is one of the most significant criteria in WSNs that can conserve energy of sensors and extend network lifetime. In this article, we propose an efficient data transmission protocol that consists in two phases of data aggregation. Our proposed protocol searches, in the first phase, similarities between measures collected by each sensor. In the second phase, it uses distance-based functions to find similarity between sets of collected data. The main goal of these phases is to reduce the data transmitted from both sensors and cluster-heads (CHs) in a clustering-based scheme network. To evaluate the performance of the proposed protocol, experiments on real sensor data from both terrestrial and underwater networks have been conducted. Compared to other existing techniques, simulation and real experimentations show that our protocol can be effectively used to reduce data transmission and increase network lifetime, while still keeping data integrity of the collected data.

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.

Reducing the data transmission in sensor networks through Kruskal-Wallis model

Data reduction is one of the most attractive way to conserve the limited energy resources of wireless sensor networks (WSNs). It aims to remove unnecessary data transmission. Therefore, data prediction and reduction mechanisms must be deployed at the source node in order to eliminate the redundant sensed data before sending them to the sink. In this paper, an energy efficient periodic distributed data reduction technique is proposed. Our technique allows each sensor node to search the variation between readings collected at each period based on the Kruskal-Wallis model. Then, the sensor selects a set of representative readings instead of sending the whole readings collected during a period to the sink. To evaluate the performance of our technique, simulations on a publicly available real sensor data followed by experiments in a real-world telosB sensor network testbed have been performed. Compared to other existing approaches, we are able to achieve up to 80% communication reduction while maintaining a high level of data accuracy.

Indoor Massive MIMO: Uplink Pilot Mitigation Using Channel State Information Map

Massive MIMO brings both motivations and challenges to develop the 5th generation mobile wireless technology. The promising number of users and the high bitrate offered per unit area are challenged by uplink pilot contamination due to pilot reuse and a limited number of orthogonal pilot sequences. This paper proposes a solution to mitigate uplink pilot contamination in an indoor scenario where multi-cell share the same pool of pilot sequences, that are supposed to be less than the number of users. This can be done by reducing uplink pilots using Channel State Information (CSI) prediction. The proposed method is based on machine learning approach, where a quantized version of Channel State Information (QCSI) is learned during estimation session and stored at the Base Station (BS) to be exploited for future CSI prediction. The learned QCSI are represented by a weighted directed graph, which is responsible to monitor and predict the CSI of User Terminals (UTs) in the local cell. We introduce an online learning algorithm to create and update this graph which we call CSI map. Simulation results show an increase in the downlink sum-rate and a significant feedback reduction.

Channel Coherence Classification with Frame-Shifting in Massive MIMO Systems

This paper considers the uplink pilot overhead in a time division duplexing (TDD) massive Multiple Input Multiple Output (MIMO) mobile systems. A common scenario of conventional massive MIMO systems is a Base Station (BS) serving all user terminals (UTs) in the cell with the same TDD frame format that fits the coherence interval of the worst-case scenario of user mobility (e.g. a moving train with velocity 300 Km/s). Furthermore, the BS has to estimate all the channels each time-slot for all users even for those with long coherence intervals. In fact, within the same cell, sensors or pedestrian with low mobility UTs (e.g. moving 1.38 m/s) share the same short TDD frame and thus are obliged to upload their pilots each time-slot. The channel coherence interval of the pedestrian UTs with a carrier frequency of 1.9 GHz can be as long as 60 times that of the train passenger users. In other words, conventional techniques waste 59-uploaded pilot sequences for channel estimation. In this paper, we are aware of the resources waste due to various coherence intervals among different user mobility. We classify users based on their coherence interval length, and we propose to skip uploading pilots of UTs with large coherence intervals. Then, we shift frames with the same pilot reused sequence toward an empty pilot time-slot. Simulation results had proved that the proposed technique overcome the performance of conventional massive MIMO systems in both energy and spectral efficiency.

Bithiophenic MALDI matrices as valuable leads for the selective detection of alkaloids

AbstractAlkaloids represent a group of biologically most interesting compounds commonly used in modern medicines but also known for exhibiting severe toxic effects. Therefore, the detection of alkaloids is an important issue in quality control of plants, dietary supplements, and herbal pharmaceutical and mostly facilitated by methods such as GC or LC-MS. However, benefitting from the development of selective matrices as well as requiring very little sample preparation, MALDI-MS may also provide a valuable supplement to these standard analytical methods. With this in mind, the present study highlights recent advances in the development of bithiophenic matrix molecules designed for the selective detection of alkaloids. Overall four new bithiophenic matrix molecules (BMs) were tested on different analytes belonging to various chemical families such as alkaloids, curcuminoids, benzopyrones, flavonoids, steroids, and peptides (I). All BMs were further compared to the commercial matrices α-cyano-4-hydroxycinnamic acid (CHCA) and 2,5-dihydroxybenzoic acid (DHB) in terms of their signal response as well as their matrix noise formation (II). Based on these results the most promising candidate, 3-(5′-pentafluorophenylmethylsulfanyl-[2,2′]bithiophenyl-5-ylsulfanyl)propionitrile (PFPT3P), was tested on highly complex samples such as the crude extracts of Colchicum autumnale, RYTMOPASC ® solution (a herbal pharmaceutical containing sparteine and rubijervine), as well as strychnine-spiked human plasma (III). For the latter, an evaluation of the limit of detection was performed. Eventually, a simplified protocol for the direct MALDI detection of major alkaloids from pulverized plant material of Atropa belladonna and Senecio vulgaris is presented (IV). Graphical abstractSelective MALDI MATRICES for Alkaloid Detection