DV-Hop Node Location Algorithm Based on GSO in Wireless Sensor Networks

Abstract

Node location is one of the most important problems to be solved in practical application of WSN. As a typical location algorithm without ranging, DV-Hop is widely used in node localization of wireless sensor networks. However, in the third phase of DV-Hop, a least square method is used to solve the nonlinear equations. Using this method to locate the unknown nodes will produce large coordinate errors, poor stability of positioning accuracy, low location coverage, and high energy consumption. An improved localization algorithm based on hybrid chaotic strategy (MGDV-Hop) is proposed in this paper. Firstly, a glowworm swarm optimization of hybrid chaotic strategy based on chaotic mutation and chaotic inertial weight updating (MC-GSO) is proposed. The MC-GSO algorithm is used to control the moving distance of each firefly by chaos mutation and chaotic inertial weight when the firefly falls into a local optimum. The experimental results show that MC-GSO has better convergence and higher accuracy and avoids the premature convergence. Then, MC-GSO is used to replace the least square method in estimating node coordinates to solve the problem that the localization accuracy of the DV-Hop algorithm is not high. By establishing the error fitness function, the linear solution of coordinates is transformed into a two-dimensional combinatorial optimization problem. The simulation results and analysis confirm that the improved algorithm (MGDV-Hop) reduces the average location error, increases the location coverage, and decreases and balances the energy consumption as compared to DV-Hop and the location algorithm based on classical GSO (GSDV-Hop).

Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks

“…Experiments verify its influence on the average localization error in three aspects: the communication radius of nodes, the percentage of anchor nodes, and the total number of nodes. The parameters used in the DEIDV-Hop experiment are shown in Table 1, while the parameter settings for PSODV-Hop [43] and GSODV-Hop [44] are identical to original references and shown in Tables 2 and 3, When designing the C-shaped area, we considered excavating a part of the square area during the simulation. The cut-out area is 30 × 70 m 2 (the original area was 100 × 100 m 2 ).…”

“…The DEIDV-Hop algorithm is compared with DV-Hop, PSODV-Hop [43], and GSODV-Hop [44] through simulation implemented on MATLAB 2014a [45], running on a desktop PC with one Intel(R) Core(TM) i5-6500 CPU @3.20 GHz processor, 8 GB RAM, and Windows 7 OS.…”

Passive Target Localization Problem Based on Improved Hybrid Adaptive Differential Evolution and Nelder-Mead Algorithm

“…Among them, TOA is the most commonly used algorithm for solving the localization problems, which can achieve high localization accuracy [26]. On the contrary, the range-free algorithms do not measure the distance or angle information; these algorithms use connectivity information in WSNs to estimate sensor positions [30]. Compared with range-based algorithms, the range-free algorithms do not require complex hardware structure.…”

“…Compared with range-based algorithms, the range-free algorithms do not require complex hardware structure. Hence, the range-free algorithms are cost effective and easy to implement; however, they are less accurate in estimating the sensor position [30].…”

Energy Efficient Range-Free Localization Algorithm for Wireless Sensor Networks

“…It is also observed from the literature that a variant of DV-Hop has utilized the evolutionary optimization algorithm to improve the localization accuracy shown in [45,46,47]. A shuffled frog leaping algorithm (SFLA) with DV-Hop is introduced by [48], in which the modified hop-size is achieved with SFLA.…”