An Energy-Efficient Secure Scheme in Wireless Sensor Networks

We propose an energy-efficient security scheme in wireless sensor networks. The proposed scheme converts sensing data using TinyMD5, which is a variation of MD5, a one-way hash function, and can solve the collision problem of hash value that occurs when MD5 is modified. In addition, it strengthens security capabilities by transmitting data through multiple paths after conversion with TinyMD5 and divides the data to make decryption of the original data difficult. To show the superiority of the proposed algorithm, we compare it with the existing schemes through simulations. The performance evaluation results show that the proposed scheme maintains security better than the existing scheme, improving the communication cost and the network lifetime

Distributed Database Management Techniques for Wireless Sensor Networks

Authors and/or their employers shall have the right to post the accepted version of IEEE-copyrighted articles on their own personal servers or the servers of their institutions or employers without permission from IEEE, provided that the posted version includes a prominently displayed IEEE copyright notice and, when published, a full citation to the original IEEE publication, including a link to the article abstract in IEEE Xplore. Authors shall not post the final, published versions of their papers.In sensor networks, the large amount of data generated by sensors greatly influences the lifetime of the network. In order to manage this amount of sensed data in an energy-efficient way, new methods of storage and data query are needed. In this way, the distributed database approach for sensor networks is proved as one of the most energy-efficient data storage and query techniques. This paper surveys the state of the art of the techniques used to manage data and queries in wireless sensor networks based on the distributed paradigm. A classification of these techniques is also proposed. The goal of this work is not only to present how data and query management techniques have advanced nowadays, but also show their benefits and drawbacks, and to identify open issues providing guidelines for further contributions in this type of distributed architectures.This work was partially supported by the Instituto de Telcomunicacoes, Next Generation Networks and Applications Group (NetGNA), Portugal, by the Ministerio de Ciencia e Innovacion, through the Plan Nacional de I+D+i 2008-2011 in the Subprograma de Proyectos de Investigacion Fundamental, project TEC2011-27516, by the Polytechnic University of Valencia, though the PAID-05-12 multidisciplinary projects, by Government of Russian Federation, Grant 074-U01, and by National Funding from the FCT-Fundacao para a Ciencia e a Tecnologia through the Pest-OE/EEI/LA0008/2013 Project.Diallo, O.; Rodrigues, JJPC.; Sene, M.; Lloret, J. (2013). Distributed Database Management Techniques for Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems. PP(99):1-17. https://doi.org/10.1109/TPDS.2013.207S117PP9

Choosing a suitable data-analytics software for a company’s operations

Abstract. In this research, the purpose was to study the different factors that contribute to a company’s consideration around choosing a suitable data-analytics software to be adopted into their operations. The research was based around the notion that there currently exists a gap between the information technology and the companies, where valuable data is being wasted by the companies at the cost of their competitiveness due to their limited capabilities in data analytics. Data-analytics software were noted to be potentially valuable for the companies by being able to help bridging the gap between them and the information technology by allowing them to make more use out of data in their operations, but this was not to be taken for granted at any situation due to the overall complexity and extent of the phenomenon. The research was conducted by performing a literature review on the existing scientific literature around the phenomenon and a case study, which provided a concrete example from a real-world setting. The combined results from these research methods were then analyzed together in a further analysis to identify relevant factors and describe their possible effects as opportunities and challenges for every company to consider, which may eventually steer their choice of a suitable data-analytics software into one direction or another. This research tries to provide better understanding around this process, which is supposed to lead to a specific choice and uncover the reasoning behind it. This can essentially present useful guidelines for the companies interested in adopting data-analytics software into their operations. The results of the research pointed out that there are plenty of different options for a company to choose from, which can prove out to be suitable for their operations. The choice itself is eventually based on the company’s own characteristics and requirements, which may require different forms of evaluations depending on their nature. In addition, it was emphasized that users should be given a central role in the consideration, because they are eventually responsible for the creation of value through data-analytics software and they are significantly being affected by the quality of the software. The opportunities and challenges also presented important points to consider, because their potential effects can easily be overlooked by many companies. The results emphasized that companies should approach the choice with careful consideration from a unique perspective, where the presented issues can essentially be utilized as useful guidelines to increase their chances of finding a suitable data-analytics software for their operations and eventually gaining value from it. However, it can be argued that data-analytics software are still surrounded with a fair amount of uncertainty relating to the companies’ return of investment, which suggests that there is still a lot of work to be done in this field

Performance assessment of real-time data management on wireless sensor networks

Technological advances in recent years have allowed the maturity of Wireless Sensor Networks (WSNs), which aim at performing environmental monitoring and data collection. This sort of network is composed of hundreds, thousands or probably even millions of tiny smart computers known as wireless sensor nodes, which may be battery powered, equipped with sensors, a radio transceiver, a Central Processing Unit (CPU) and some memory. However due to the small size and the requirements of low-cost nodes, these sensor node resources such as processing power, storage and especially energy are very limited. Once the sensors perform their measurements from the environment, the problem of data storing and querying arises. In fact, the sensors have restricted storage capacity and the on-going interaction between sensors and environment results huge amounts of data. Techniques for data storage and query in WSN can be based on either external storage or local storage. The external storage, called warehousing approach, is a centralized system on which the data gathered by the sensors are periodically sent to a central database server where user queries are processed. The local storage, in the other hand called distributed approach, exploits the capabilities of sensors calculation and the sensors act as local databases. The data is stored in a central database server and in the devices themselves, enabling one to query both. The WSNs are used in a wide variety of applications, which may perform certain operations on collected sensor data. However, for certain applications, such as real-time applications, the sensor data must closely reflect the current state of the targeted environment. However, the environment changes constantly and the data is collected in discreet moments of time. As such, the collected data has a temporal validity, and as time advances, it becomes less accurate, until it does not reflect the state of the environment any longer. Thus, these applications must query and analyze the data in a bounded time in order to make decisions and to react efficiently, such as industrial automation, aviation, sensors network, and so on. In this context, the design of efficient real-time data management solutions is necessary to deal with both time constraints and energy consumption. This thesis studies the real-time data management techniques for WSNs. It particularly it focuses on the study of the challenges in handling real-time data storage and query for WSNs and on the efficient real-time data management solutions for WSNs. First, the main specifications of real-time data management are identified and the available real-time data management solutions for WSNs in the literature are presented. Secondly, in order to provide an energy-efficient real-time data management solution, the techniques used to manage data and queries in WSNs based on the distributed paradigm are deeply studied. In fact, many research works argue that the distributed approach is the most energy-efficient way of managing data and queries in WSNs, instead of performing the warehousing. In addition, this approach can provide quasi real-time query processing because the most current data will be retrieved from the network. Thirdly, based on these two studies and considering the complexity of developing, testing, and debugging this kind of complex system, a model for a simulation framework of the real-time databases management on WSN that uses a distributed approach and its implementation are proposed. This will help to explore various solutions of real-time database techniques on WSNs before deployment for economizing money and time. Moreover, one may improve the proposed model by adding the simulation of protocols or place part of this simulator on another available simulator. For validating the model, a case study considering real-time constraints as well as energy constraints is discussed. Fourth, a new architecture that combines statistical modeling techniques with the distributed approach and a query processing algorithm to optimize the real-time user query processing are proposed. This combination allows performing a query processing algorithm based on admission control that uses the error tolerance and the probabilistic confidence interval as admission parameters. The experiments based on real world data sets as well as synthetic data sets demonstrate that the proposed solution optimizes the real-time query processing to save more energy while meeting low latency.Fundação para a Ciência e Tecnologi