Dynamic Wireless Sensor Networks

Covering a specific field and transferring data to Base Station (BS) is a real defiance. Although there are extended efforts to build a routing protocol that avoids a high energy consumption, the dynamic nature and complex environments of most of WSN recent application makes building such protocol a big challenge. To avoid energy exhaustion, many machine learning algorithms are used to manage the network operations. We proposed a new model to optimize the coverage requirements in WSNs to provide continuous monitoring of specified targets for longest possible time with limited energy resources. Moreover, we allow sensor nodes to move to appropriate positions to collect environmental information. The proposed model is based on the continuous and variable speed movement of mobile sensors to keep all targets under their cover all times. To further prove that the proposed model is better than other related work, a set of experiments in different working environments and a comparison with the most related work are conducted.

Ideally, the lifetime of a homogeneous WSN is maximized when the remaining energy of nodes in the network remains the same. However, most of WSN applications in harsh and complex environments require a kind of nodes heterogeneity, i.e., node mobility; to extend the network coverage and lifetime. In homogeneous WSN, clustering protocols assumed that all the sensor nodes are supplied with the same characteristics, i.e., initial energy. However, placing few heterogeneous nodes in WSN, such as nodes with more computing powers, is an effective way to increase network lifetime and reliability. In this chapter, we propose a sensor clustering method for dynamically organizing heterogeneous WSN using Genetic Algorithm. Moreover, we propose a set of key heterogeneity factors that enhance the performance of WSNs in harsh environments.

Generally, WSN consists of thousands of inexpensive devices, called sensor nodes, capable of computation, communication and sensing events in a specific environment [1‐3]. WSNs have attracted intensive interest from both academia and industry due to their wide application in civil and military scenarios [4‐6]. Enormous advances that are emerging in WSNs act as a revolution in all aspects of our life. WSNs have unique specifications describe it and different from other networks. Sensor nodes have energy and computational challenges. Moreover, WSNs may be prone to software failure, unreliable wireless connections, malicious attacks, and hardware faults; that make the network performance may degrade significantly over time. Recently, there is a great interest related to routing process in WSNs using intelligent and machine learning algorithms such as Genetic Algorithms [7‐9]. Security aspects in routing protocols have not been given enough attention, since most of the routing protocols in WSNs have not been designed with security requirements in mind [10‐14]. In this chapter, the main models of WSN with their advantages and limitations are discussed, specially the clustering model. In addition, it provides a literature of the existing clustering methods of WSN that aims to increase the network lifetime. After that, the security aspects are explained in details. Finally, the existing secure clustering methods are discussed and evaluated based on a set of criteria.

To extend the longevity of a homogeneous WSN, the key is to avoid nodes deplete energy before the others. Accordingly, this chapter proposes a new clustering model for WSN used in dynamic environments. In each transmission round, the remaining energy of sensor nodes are fairly even with some fluctuations. That is, as a consequence of the proposed method, the variance among remaining energy is quite low, which implies that the sensor nodes shared the burden of relaying messages and, hence, elongated the overall network life. The main factors that are used in our proposed method for choosing a CH are the distance between the CH and BS, the remaining battery power, and the expected consumed energy.

In wireless sensor networks (WNSs), the amount of transferred data is mainly depending on the network lifetime. Hence, the network throughput can be maximized by extending the network lifetime as long as possible. Accordingly, the clustering model is proposed to extend the network lifetime and improve the network performance. However, the optimum network structure in that model may differs from round to round depending on a set of sensor nodes characteristics, i.e, their remaining energy. Getting the intended optimum structure is non trivial process, which includes determining the appropriate number of clusters, electing a cluster head (CH) for each cluster, and assigning each sensor node to a clusters. For that, a new Genetic Algorithm (GA) based model is proposed to form the network structure that optimize its throughput.

Building a secure routing protocol in WSN is not trivial process. Thee are two main types of security attacks against WSNs: active and passive. WSN as a new category of computer-based computing platforms and network structures is showing new applications in different areas such as environmental monitoring, health care and military applications. Although there are a lot of secure data transmission schemes designed for data aggregation and transmission over a network, the limited resources and the complex environment make it invisible to be used with WSNs. Furthermore, secure data transmission is a big challenging issue in WSNs especially for the application that uses image as its main data such as military applications. This problem is mainly related to the limited resources and data processing capabilities. This chapter introduces a secure data processing and transmission schema in WSN. The chapter reviewed and critically discussed the most prominent secure clustering routing algorithms that have been developed for WSNs. Then, we explained the guidelines and the steps towards building a simple solution for securing the dynamic cluster network while consuming as little energy as possible and is adapted to a low computing power. Moreover, four phased towards building a secure clustering algorithm for WSN are proposed. These phases are secure cluster head selection, secure cluster formation, secure data aggregation by the cluster head from its cluster nodes, and secure data routing to the base station. Also, the chapter proposes and applies an evaluation criteria for the existing secure clustering algorithms.

Building a secure routing protocol in WSN is not trivial process. It looks like an optimization process through which we try to find the optimum solution that maximize the network performance in an environment with a set of complicated constraints. The main purpose is not only to design new routing protocol that guarantee the network efficiency, but also balancing between this efficiency and the security requirements. For that purpose, we designed and followed a general framework to simplify the process of building such that protocol. In this chapter, an overview of the working steps towards building the proposed protocol is described. Then the protocol objectives and methodology are discussed.

SCADA systems are network presence systems that face significant threats and attacks. After an attack occurred, SCADA requires forensic investigation to understand the cause and effects of the intrusion or disruption on the systems services. However, forensic investigators cannot turn it off during acquiring the live data that is required for the investigation and analysis process. That is because the systems services need to be continuously operational. Despite the great efforts to acquire live data on SCADA systems, the continuously change of this type of data and the risk on the systems services make it a big challenge. In this proposal, we suggest a new method to acquire live data on a SCADA system using wireless sensor network. The proposed idea attempts to monitor file integrity and acquire live data in a way that minimizes risk to the systems services. In addition, it aims to help Forensic investigators by guarantee early data acquisition after incident and digital evidence validity as well.