Seyyit Alper Sert

MOFCA: Multi-objective fuzzy clustering algorithm for wireless sensor networks

Abstract This study introduces a new clustering approach which is not only energy-efficient but also distribution-independent for wireless sensor networks (WSNs). Clustering is used as a means of efficient data gathering technique in terms of energy consumption. In clustered networks, each node transmits acquired data to a cluster-head which the nodes belong to. After a cluster-head collects all the data from all member nodes, it transmits the data to the base station (sink) either in a compressed or uncompressed manner. This data transmission occurs via other cluster-heads in a multi-hop network environment. As a result of this situation, cluster-heads close to the sink tend to die earlier because of the heavy inter-cluster relay. This problem is named as the hotspots problem. To solve this problem, some unequal clustering approaches have already been introduced in the literature. Unequal clustering techniques generate clusters in smaller sizes when approaching the sink in order to decrease intra-cluster relay. In addition to the hotspots problem, the energy hole problem may also occur because of the changes in the node deployment locations. Although a number of previous studies have focused on energy-efficiency in clustering, to the best of our knowledge, none considers both problems in uniformly and non-uniformly distributed networks. Therefore, we propose a multi-objective solution for these problems. In this study, we introduce a multi-objective fuzzy clustering algorithm (MOFCA) that addresses both hotspots and energy hole problems in stationary and evolving networks. Performance analysis and evaluations are done with popular clustering algorithms and obtained experimental results show that MOFCA outperforms the existing algorithms in the same set up in terms of efficiency metrics, which are First Node Dies (FND), Half of the Nodes Alive (HNA), and Total Remaining Energy (TRE) used for estimating the lifetime of the WSNs and efficiency of protocols.

Evolutionary Multiobjective Query Workload Optimization of Cloud Data Warehouses

With the advent of Cloud databases, query optimizers need to find paretooptimal solutions in terms of response time and monetary cost. Our novel approach minimizes both objectives by deploying alternative virtual resources and query plans making use of the virtual resource elasticity of the Cloud. We propose an exact multiobjective branch-and-bound and a robust multiobjective genetic algorithm for the optimization of distributed data warehouse query workloads on the Cloud. In order to investigate the effectiveness of our approach, we incorporate the devised algorithms into a prototype system. Finally, through several experiments that we have conducted with different workloads and virtual resource configurations, we conclude remarkable findings of alternative deployments as well as the advantages and disadvantages of the multiobjective algorithms we propose.

Impacts of routing attacks on Surveillance Wireless Sensor Networks

Surveillance Wireless Sensor Networks (SWSNs) are the result of abundant data gathering requirements occurring in Wireless Sensor Networks, specifically for surveillance reasons. Most SWSNs operate by sensing the environment and transmitting the acquired data to a sink in order to utilize it for decision making processes such as object detection, classification, localization, or event detection. In this respect, secure routing of acquired data is crucial since a decision making process is performed according to the received data. Although there are various other attack types targeting different layers of the protocol stack, in this study we primarily focus on routing attacks occurring in the network layer, highlight possible defense mechanisms with respect to each attack type, and present impacts of routing attacks on SWSNs.

Data fusion and processing in Wireless Multimedia Sensor Networks: An analysis for surveillance applications

Wireless Sensor Network implementations move toward multimedia applications which necessitate more energy consumption every other day. Special sensor networks designed primarily for the delivery of multimedia content are called Wireless Multimedia Sensor Networks. In these networks, deployed nodes may change their places with or without user requirement which results in a non-uniform distribution. In this context, data fusion and processing in Wireless Multimedia Sensor Networks become crucial when considering application domains and readily available resources. Surveillance applications domain is among the popular implementation areas of Wireless Multimedia Sensor Networks. In this paper, an analysis on the state-of-the art data fusion and processing in Wireless Multimedia Sensor Networks targeting surveillance applications is presented and possible future work is stated.

Security attacks and countermeasures in Surveillance Wireless Sensor Networks

Surveillance has always been an important task for human beings either for protecting a precious asset or gathering information from the surrounding environment. However, things to be monitored are increasing with a huge rate due to the changing requirements day by day. As a result, it becomes nearly impossible for individuals to do this task manually. To be able to fulfill such requirements, Surveillance Wireless Sensor Networks (SWSNs) have emerged. An SWSN is comprised of tiny nodes geared with various sensor types from a few of them to hundreds or even thousands, where each node is connected to several surrounding nodes. Most SWSNs operate by sensing at the node-level and conveying the sensed data to the sink (base station) for decision making processes such as object classification, localization, or event detection. In this respect, secure transmission of the sensed data is crucial since decision making is performed according to the received data. In this study, we primarily discuss the security attacks and defense mechanisms in surveillance wireless sensor networks. Moreover, security requirements of SWSNs, how accuracy and efficiency aspects of SWNSs are affected from each attack type, and evaluation criteria of defense mechanisms are presented in detail.

Effects of selective forwarding attacks on wireless sensor network efficiency

Wireless Sensor Networks (WSNs) facilitate efficient data gathering requirements occurring in indoor and outdoor environments. A great deal of WSNs operates by monitoring the area-of-interest (AOI) and transmitting the obtained data to a sink. The transmitted data can be utilized for object detection, object classification, instance localization, and high level semantic information extraction processes. In this regard, security of raw and relayed data is both crucial and susceptible to malicious attempts targeting the efficiency of the network. In this study, we focus on the effects of selective forwarding attacks taking place in the network layer and highlight possible impacts of successful attacks through experiment. Our analysis and evaluations are done on a selected sensor network information fusion architecture. According to the obtained results, selective forwarding attacks are hard to detect and have a significant effect on the efficiency of wireless sensor networks.

An efficient fuzzy path selection approach to mitigate selective forwarding attacks in wireless sensor networks

Wireless Sensor Networks (WSNs) facilitate efficient data gathering requirements occurring in indoor and outdoor environments. A great deal of WSNs operates by sensing the area-of-interest (AOI) and transmitting the obtained data to a sink/(s). The transmitted data is then utilized in decision making processes. In this regard, security of raw and relayed data is both crucial and susceptible to malicious attempts targeting the task of the network which occurs on the wireless transmission medium. A node, when compromised, may deliberately forward data packets selectively. When this happens, nodes adjacent to the malicious nodes cannot identify the malevolent node and mitigate the effects of the attacks. In this study, we introduce a fuzzy path selection approach that efficiently mitigates single selective forwarding attacks in WSNs. Performance of our proposed approach and its evaluations are simulated and obtained. Our experimental results show that our approach is an effective solution to serve as a defense mechanism in terms of the efficiency metrics, such as Half of the Nodes Alive (HNA), Total Remaining Energy (TRE), and Packet Drop Ratio (PDR).

Fuzzy processing in surveillance wireless sensor networks

This paper introduces a new distributed fuzzy network clustering approach that specifically targets surveillance applications of wireless sensor networks. Surveillance domain heavily consists of multimedia applications which require heavy energy consumption. Moreover, in sensor networks, positioned nodes can be relocated either by users or external events which becomes crucial if the domain and existing resources include uncertainties. In this study, a distributed fuzzy clustering approach is introduced and then experimentally evaluated. The obtained results on the effect of fuzzy processing in heterogeneous sensor networks are presented.

Improving Hadoop Hive Query Response Times Through Efficient Virtual Resource Allocation

The performance of the MapReduce-based Cloud data warehouses mainly depends on the virtual hardware resources allocated. Most of the time, the resources are values selected/given by the Cloud service providers. However, setting the right virtual resources in accordance with the workload demands of a query, such as the number of CPUs, the size of RAM, and the network bandwidth, will improve the response time when querying large data on an optimized system. In this study, we carried out a set of experiments with a well-known Mapreduce SQL-translator, Hadoop Hive, on benchmark decision support the TPC benchmark (TPC-H) database in order to analyze the performance sensitivity of the queries under different virtual resource settings. Our results provide valuable hints for the decision makers who design efficient MapReduce-based data warehouses on the Cloud.

An efficient fuzzy fusion-based framework for surveillance applications in Wireless Multimedia Sensor Networks

This study is focused on a new approach for addressing the trade-off between accuracy and energy-efficiency of Wireless Multimedia Sensor Networks. Although a number of previous studies have focused on various special topics in Wireless Multimedia Sensor Networks in detail, to best of our knowledge, none presents a fuzzy multi-modal data fusion system, which is light-weight and provides a high accuracy ratio. Especially, multi-modal data fusion targeting surveillance applications make it inevitable to work within a multi-level hierarchical framework. In this study, we primarily focus on accuracy and efficiency by utilizing such a framework. In order to evaluate the performance of the proposed framework, a set of experiments is conducted and obtained results are presented.