A Fusion-Based Framework for Wireless Multimedia Sensor Networks in Surveillance Applications

Multimedia sensors enable monitoring applications to obtain more accurate and detailed information. However, the development of efficient and lightweight solutions for managing data traffic over wireless multimedia sensor networks (WMSNs) has become vital because of the excessive volume of data produced by multimedia sensors. As part of this motivation, this paper proposes a fusion-based WMSN framework that reduces the amount of data to be transmitted over the network by intra-node processing. This framework explores three main issues: 1) the design of a wireless multimedia sensor (WMS) node to detect objects using machine learning techniques; 2) a method for increasing the accuracy while reducing the amount of information transmitted by the WMS nodes to the base station, and; 3) a new cluster-based routing algorithm for the WMSNs that consumes less power than the currently used algorithms. In this context, a WMS node is designed and implemented using commercially available components. In order to reduce the amount of information to be transmitted to the base station and thereby extend the lifetime of a WMSN, a method for detecting and classifying objects on three different layers has been developed. A new energy-efficient cluster-based routing algorithm is developed to transfer the collected information/data to the sink. The proposed framework and the cluster-based routing algorithm are applied to our WMS nodes and tested experimentally. The results of the experiments clearly demonstrate the feasibility of the proposed WMSN architecture in the real-world surveillance applications

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Previous advances in Information Technologies and especially in Micro Electro-Mechanical Systems, have made the Production and deployment of tiny, battery-powered nodes communicating over wireless links possible. Networks comprised of such nodes with sensing capability are called Wireless Sensor Networks. The early deployment aim was to use these nodes only in a passive way for indoor applications. These kinds of early nodes had the ability to sense scalar data such as temperature, humidity, pressure and location of surrounding objects. However, recently available sensor nodes have higher computation capability, higher storage space and better power solutions with respect to their predecessors. With these developments in addition to scalar data delivery, multimedia content has become the core focus. A wireless sensor network with multimedia capabilities is calledWireless Multimedia Sensor Network. There has always been a trade-o between accuracy and energy-e ciency in these new generation networks because of their resource-constrained nature. In this thesis we introduce a new approach to address this trade-o in Wireless Multimedia Sensor Networks. Although a number of previous studies have focused on various special topics in Wireless Multimedia Sensor Networks in detail, to the best of our knowledge, none presents a fuzzy multi-modal data fusion system, which is light-weight and provides a high accuracy ratio. Especially, a multi-modal data fusion system targeting surveillance applications make it inevitable to work within a multi-level hierarchical framework. In this thesis, our primary focus is on accuracy and e ciency by utilizing our framework. Along with the fuzzy fusion framework, a new fuzzy clustering algorithm, namely Multi-Objective Fuzzy Clustering Algorithm (MOFCA), is introduced and evaluated in detail as well.M.S. - Master of Scienc

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Technological developments have made the generation and usage of wireless sensor nodes possible. Although an individual node is capable of gathering data alone, these nodes generally cooperate to extract high-level semantic information from the sensed region. Networks consisting such nodes are referred to as Wireless Sensor Networks (WSNs). There is generally a balance between energy-efficiency and accuracy, which are two desirable but incompatible features of these networks, because of the resource-restricted nature of the utilized devices. The balance, which can also be called the trade-off, is tried to be optimized by efficient algorithms that mostly utilize manually-value-assigned parameters through trial-and-error processes. However, this assignment process nearly always fails in finding the optimum blend of parameters, renders the implementation vague and inapplicable for most cases, and generally biases the obtained result. In this dissertation, an Intelligent Fuzzy Clustering Approach for Energy-Efficient Data Aggregation in Wireless Sensor Networks is proposed. The proposed approach is a distribution-agnostic approach that runs and scales efficiently for sensor network applications. Additionally, along with the proposal, an optimization framework is utilized to tune the parameters used in the fuzzy clustering process in order to optimize the performance of a given WSN. This dissertation also includes performance comparisons and experimental evaluations of the proposal with the selected state-of-the-art algorithms. The experimental results reveal that the proposal performs better than any of the compared protocols under the same network setup considering metrics used for comparing energy-efficiency and network lifespan of the protocols. Besides, along with the proposed optimized fuzzy network clustering protocol, an empirical study on multi-modal object classification problem in wireless sensor networks is conducted in detail and obtained results are presented as well in order to corroborate the object classification accuracy aspect of the proposed protocol.Ph.D. - Doctoral Progra

Optimizing the Performance of Rule-Based Fuzzy Routing Algorithms in Wireless Sensor Networks

© 2019 IEEE.Effective data routing is one of the crucial themes for energy-efficient communication in wireless sensor networks (WSN). In the WSN research domain, fuzzy approaches are in most cases superior to well-defined methodologies, especially where boundaries between clusters are unclear. For this reason, a significant number of studies have recently proposed fuzzy-based solutions for the problems encountered in WSNs. Rule-based fuzzy systems are part of these widespread fuzzy-based solutions that often involve some field experts for identification and derivation of fuzzy rules as well as fuzzy membership functions; thus, a considerable amount of time is devoted to the realization of the final system. Nevertheless, it is almost impossible or not feasible to realize a fuzzy system with an optimality property. In this study, we utilize the modified clonal selection algorithm (CLONALG-M) to improve the performance of rule-based fuzzy routing algorithms. Although previous studies have been devoted to fuzzy optimization in general, to the best of our knowledge, improving the efficiency of rule-based fuzzy routing algorithms has not yet been considered. For this reason, CLONALG-M is applied to determine the approximate form of the output membership functions that improve the overall performance of fuzzy routing algorithms, whose rule base and shapes of membership functions are initially known. Experimental analysis and evaluations of the approach used in this study are performed on selected fuzzy rule-based routing algorithms and the obtained results verify that our approach performs and scales well to improve fuzzy routing performance

Increasing energy efficiency of rule-based fuzzy clustering algorithms using CLONALG-M for wireless sensor networks

© 2021 Elsevier B.V.Because of its efficiency, clustering is used for effective communication in Wireless Sensor Networks (WSNs). In the WSN clustering area, fuzzy approaches are found to be superior to crisp cluster counterparts when the boundaries between clusters are unclear. As a result, many studies have proposed some fuzzy-based solutions to the cluster problem in WSNs. Most rule-based fuzzy clustering systems employ field experts in trial and error processes, identifying and defining fuzzy rules as well as the forms of membership functions at the output; thus, considerable time has been allocated to realize and define these functions. Therefore, it is almost impossible or impractical to achieve a fuzzy system optimally. In this study, we propose a modified clonal selection algorithm (CLONALG-M) to improve the energy efficiency of rule-based fuzzy clustering algorithms. Although some studies in the literature focus on fuzzy optimization in general, to the best of our knowledge, performance improvement of rule-based fuzzy clustering algorithms is not taken into account. The CLONALG-M algorithm based on the Clonal Selection Principle is used to elucidate the basic principles of an adaptive immune system. In this study, we apply this principle to determine the approximate deployment of output-based membership functions that increase the performance of rule-based fuzzy clustering algorithms, whose rule base and shape of membership functions are previously known. Experimental analysis and evaluations of the proposed approach have been performed on selected fuzzy clustering approaches, and obtained results show that our approach performs and adapts well for improving performance of fuzzy output functions

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 might yield a non-uniform deployment in the long run. In this regard, fuzzy processing 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

A Two-Tier Distributed Fuzzy Logic Based Protocol for Efficient Data Aggregation in Multihop Wireless Sensor Networks

This study proposes a two-tier distributed fuzzy logic based protocol (TTDFP) to improve the efficiency of data aggregation operations in multihop wireless sensor networks (WSNs). Clustering is utilized for efficient aggregation requirements in terms of consumed energy. In a clustered network, member (leaf) nodes transmit obtained data to cluster-heads (CHs) and CHs relay received packets to the base station. In multihop wireless networks, this CH-generated transmission occurs over other CHs. Due to the adoption of a multihop topology, hotspots and/or energy-hole problems may arise. This article proposes a TTDFP to extend the lifespan of multihop WSNs by taking the efficiency of clustering and routing phases jointly into account. TTDFP is a distribution-adaptive protocol that runs and scales sensor network applications efficiently. Additionally, along with the two-tier fuzzy logic based protocol, we utilize an optimization framework to tune the parameters used in the fuzzy clustering tier in order to optimize the performance of a given WSN. This paper also includes performance comparisons and experimental evaluations with the selected state-of-the-art algorithms. The experimental results reveal that TTDFP performs better than any other protocols under the same network setup considering metrics used for comparing energy-efficiency and network lifespan of the protocols

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