Mobile Anchor-Assisted Localization Using Invasive Weed Optimization Algorithm

Mobile Anchor-Assisted Localization Using Invasive Weed Optimization Algorithm

Vaishali Raghavendra Kulkarni, Veena Desai, Akash Sikarwar, Raghavendra V. Kulkarni
DOI: 10.4018/978-1-7998-3222-5.ch017
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Sensor localization in wireless sensor networks has been addressed using mobile anchor (MA) and a metaheuristic algorithm. The path of a MA plays an important role in localizing maximum number of sensor nodes. The random and circle path planning methods have been presented. Each method has been evaluated for number of localized nodes, accuracy, and computing time in localization. The localization has been performed using trilateration method and two metaheuristic stochastic algorithms, namely invasive weed optimization (IWO) and cultural algorithm (CA). Experimental results indicate that the IWO-based localization outperforms the trilateration method and the CA-based localization in terms of accuracy but with higher computing time. However, the computing speed of trilateration localization is faster than the IWO- and CA-based localization. In the path-planning algorithms, the results show that the circular path planning algorithm localizes more nodes than the random path.
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1. Introduction

A wireless sensor network (WSN) is a collection of thousands of small sensor nodes used for several monitoring and tracking applications. Applications of WSNs include traffic surveillance, environmental monitoring, military and health applications and wildfire detection (Mohamed et al., 2018). The location of a sensor is a geographically meaningful information in a WSN. The data generated in a WSN node is useful only if the location of the node is known. For example, emergency services in disasters, such as fire, landslide or accidents can be made available if the geographical coordinates of accidents are known. Some routing protocols use the location information as an important decision parameter. Localization is a process aimed to determine the location of the sensor nodes in WSNs (Paul and Sato, 2017). In many WSNs, localization is performed using special sensor nodes referred as beacons or anchors that are aware of their geographical location. The nodes that are not aware of their location are referred to as unknown, dumb or target nodes. The installation of global positioning system (GPS) adds to the cost and size of the hardware on tiny sensors. Therefore, it is not practical to load all the sensor nodes with GPS hardware, but use as small number of beacons as possible in WSN localization. The use of a minimum number of beacons is a critical issue in sensor localization. The most commonly used alternative to static beacons is to use a GPS-equipped mobile anchor (MA) that travels in the WSN. The MA broadcasts its current position to localize the dumb nodes in its communication range. The coordinates broadcasted by an MA are referred as anchor points (Erdemir et al., 2018). A WSN with static unknown nodes and a MA is depicted in Figure 1. Localization process takes place in two phases. In the first phase, a dumb node checks whether it is in the vicinity of an MA and records the anchor points and measures the distance from itself and the anchor points. In the second phase, the anchor points and distance measurement values are used to determine the locations. For the distance measurement, several metrics, such as received signal strength indication (RSSI), angle of arrival, time of arrival, time difference of arrival or communication range method have been presented in previous research (Alrajeh et al., 2013). The different geometric methods, such as trilateration, multilateration, triangulation, bounding box, distance vector hop routing etc. are used for location estimation. The localization process can be centralized or distributed, range-based or range-free, anchor-based or anchor-free.

Use of mobility for the sensor nodes in WSN plays an important role. Mobile WSNs (MWSNs) are useful in tracking movement in applications such as traffic surveillance, animal habitat, package tracking etc. (Mohamed et al., 2017). Localization is more challenging in MWSN than static. There is a need of dynamic navigation in MWSNs as the sensors frequently change their positions. Unlike in static WSNs, there is a need for frequent localization in MWSNs, which consumes more time and energy. Usually, centralized localization is not preferred in MWSNs because it takes more time and involves communication overhead.

Figure 1.

MA and unknown nodes in WSN


In a static WSN, the localization process is performed only once, immediately after the deployment. Whereas, in an MWSN, the continuously travelling nodes require frequent localization. Mobility results in extra time and energy utilization. The communication may be unreliable due to ad hoc network. If the sink node is travelling at different locations, it is also necessary that the number of anchors should be available for localization process. In traditional routing protocols for WSNs, the routing tables are fixed, and they get refreshed after a certain amount of time. In case of a mobile WSN, the routing tables get outdated very fast and new entries in routing tables must be stored frequently. This increases the time, energy and cost of localization. The frequent localization requires higher capacity of batteries with more lifetime (Chelouah et al., 2018).

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