Performance Analysis of Hierarchical Routing Protocols in Heterogenous WSNs

Performance Analysis of Hierarchical Routing Protocols in Heterogenous WSNs

Yassine Yazid (National School of Applied Sciences of Tangier, Morocco & Abdelmalek Essaadi University, Morocco), Imad Ezzazi (National School of Applied Sciences of Fes, Morocco & Université Sidi Mohamed Ben Abdellah, Morocco), Mounir Arioua (National School of Applied Sciences of Tetouan, Morocco & Abdelmalek Essaadi University, Morocco) and Ahmed El Oualkadi (National School of Applied Sciences of Tangier, Morocco & Abdelmalek Essaadi University, Morocco)
DOI: 10.4018/978-1-7998-0117-7.ch008
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Since the appearance of WSN, the energy efficiency has been widely considered as a critical issue due to the limited battery-powered nodes. In this regard, communication process is the most energy demanding in sensor nodes. Subsequently, using energy-aware routing protocols in order to decrease the communications costs as much as possible and increase the network lifetime is of paramount importance. In this chapter, we have mainly focused on the most recent-based clustered routing algorithms for heterogeneous WSNs, namely Selected Election Protocol (SEP), and Distributed Energy Efficient Clustering Protocol (DEEC). In addition, we have proposed an efficient clustered routing protocol based on Zonal SEP algorithm. Indeed, we have evaluated the performance of the proposed protocol according to different scenarios in order to guarantee the best distribution of heterogeneous nodes in the network. The results have shown that the proposed clustered routing approach outperforms the existed Z-SEP protocol in terms of energy efficiency and stability.
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Over the past decades, wireless sensor networks (WSNs) have become an emerging technology through their increasing use in various fields. This is mainly due to their countless advantages, including the ease of network deployment, simplified management and fast communications, that have attracted significant attention in many applications, i.g. medical monitoring, environmental, military and surveillance applications. Generally, a typical WSN combines a group of tiny autonomous devices called sensor nodes which are absolutely dispersed in different ways, randomly by planes or drones. For instance, nodes can be distributed randomly in a forest fire zone in order to create a suitable temperature map to facilitate its tracking and extinguishing, or they are manually installed in specific locations in a building or in a human body to monitor environmental and physical conditions such as temperature and humidity. After sensing process, each concerned sensor node transmits the data through the network to a collection data base called a base station or sink. Generally, WSN suffers from major restrictions that make it difficult to achieve higher levels of performance and efficiency. In particular, there are many common constraints such as the node's weak storage capacity, low processing power, unstable network topology, interference and energy stress. However, energy is classified as a major problem in WSN since sensor nodes rely on small batteries with limited power supply. The autonomous and limited batteries directly affect the energy resources of the sensors, when nodes process and communicate the data as well as retransmit data due to channel impairments. This results in a significant transmission energy consumption with a delay in the response time. Overall, ensuring effective fault tolerance techniques can mitigate these problems. However, the fact that sensor nodes are limited in terms of power supply and bandwidth makes designing energy efficient techniques for WSN a challenging task for the research community.

Sensor nodes can exhaust their energy when processing and transmitting data in a wireless environment. Particularly, the radio communication is the main responsible of high energy dissipation and short network lifetime. Therein, the cost of a jump in terms of energy is measured by the distance between two nodes involved in the communication (transmitter and receiver). Hence, the destination can be reached either with many small jumps called multiple jumps, or with a small number of large jumps called single jumps. Whereas, the overall cost of routing is the sum of the energies consumed at all jumps, hence multi-hop routing has been deemed to be more efficient than single-hop routing (Fedor & Collier, 2007). Therefore, the amount of the consumed energy is proportional to the length of jumps. Hence, the longer the jumps between transceivers are, the higher the amount of energy consumed will be. Thus, to overcome these constraints, several methods and techniques have been used to obtain better results in term of energy efficiency. Accordingly, to control the energy of nodes it is highly required to resort to energy efficient methods to relay data from the sender node to the receiver one (i.e. Relay node or Base station) which guarantees a prolonged network lifetime. For this reason, routing approaches have recently been considered as one of the most promising energy efficiency techniques employed in WSN networks to reduce the energy burdens of communications. Principally, the routing techniques are categorized into three classes depending on network structure: flat, location-based and hierarchical architectures. The hierarchical (i.e. clustering) routing architectures have been largely used in WSN due to their energy efficiency and load balancing in the network compared to other techniques (Al-Karaki & Kamal, 2004). This method plays an important role to remedy the energy constraints in WSN. It is a technique of partitioning for high-density networks into subgroups of nodes called clusters. Thereby, the cluster formation allows a better management of the routing on the network. Each subset (cluster) consists of many sensor nodes grouped around a leader node which is considered as the collector node of the cluster. In addition, the collector nodes named cluster-heads (CHs) coordinate and order the activities in the cluster. The transmissions power is adjusted by allowing certain nodes to set on sleep mode, or by organizing the transmission instances to avoid collisions.

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