Multi-objective optimization for a reliable localization scheme in wireless sensor networks

Underwater acoustic communication is a method of sending and receiving messages beneath the water’s surface that has been extensively researched for long-distance communication and localization. While, in this paper we will focus on medium range optical communication localization and short range Magneto Inductive (MI) communication localization. Both optical wireless sensor networks (OWSNs) and magneto inductive wireless sensor networks (MIWSNs) are investigated for localization of sensor nodes using a received signal strength indicator (RSSI) based localization technique for underwater environment/medium. Location information for MI nodes is obtained directly from mutual coupling measurement with a proposed localization scheme. Where as the localization of optical sensor nodes is done by exploiting the Lambert cosine law which gives values for luminous intensity in different distances and orientations. A simulation environment for both underwater optical wireless sensor networks (UOWSNs), and underwater magneto inductive wireless sensor networks (UMIWSNs) consisting of eight Tx anchor nodes and ten Rx sensor nodes is created to elaborate the localization estimation results.

In wireless sensor networks, sensor node localization is an important topic because sensor nodes are randomly scattered in the region of interest and they get connected into the network on their own. Localization information is needed in routing of data, reducing energy consumption and in location dependent data acquisition. We propose a localization scheme plus a medium access protocol that: (a) reduces power consumption; (b) does not require high power beacons; (c) provides better localization accuracy; and (d) helps in reducing collisions. The proposed localization scheme is based on received signal strength by the three masters. Moreover, it is a distributed algorithm.

Ultra-wideband (UWB) technology has the characteristics which other wireless communication technology can not match to. It combines with wireless sensor network (WSN) technology, using in the environment of coal mine underground, to achieve safety monitoring by WSN node location. Using appropriate location algorithm can not only improve the location accuracy of the calculation can also reduce system cost and power consumption. This paper mainly describes the basic nodes location principle and algorithm of WSN, proposes the location algorithm which is suitable for coal mine environment, and improves it based on traditional ranging and location method. Simulation results show that the improved location algorithm effectively improves location accuracy and location performance of the node.

With the development of electronic technology and communication protocols, wireless sensor network technology is developing rapidly. In a sense, the traditional static wireless sensor network has been unable to meet the needs of new applications. However, the introduction of mobile nodes extends the application of wireless sensor networks, despite the technical challenges. Because of its flexibility, the mobile wireless sensor network has attracted great attention, and even small, self-controlled mobile sensor devices have appeared. At present, mobile node localization has become one of the hotspots in wireless sensor networks. As the storage energy of wireless sensor network nodes is limited, and the communication radius is small, many scientists have focused their research direction on the location algorithm of mobile nodes. According to the continuity principle of mobile node movement, in this paper we propose an improved mobile node localization algorithm based on the Monte Carlo Location (MCL) algorithm, and the method can reduce the sampling interval effectively. First of all, this paper introduces the structure and classification of wireless sensor localization technology. Secondly, the principle of the Monte Carlo Location algorithm is described in detail. Thirdly, we propose an efficient method for mobile node localization based on the MCL algorithm. Finally, the effectiveness and accuracy of the new algorithm are verified by comparative analysis.

Pattern transformation-invariant schemes for wireless sensor networks based on an edge detection gradient-based mechanism

In the most applications of wireless sensor networks (WSN), the position of sensor node is important for the environment sensing, search and rescue, and geographical routing and tracking, and so on. This paper has proposed an accurate and simply scheme of mobile-assisted localization for the wireless channel loss model unknown environment in WSN. One localization algorithm is implemented using the particle swarm optimization (PSO). For improved the localization effect, the path planning strategy based on a kind of grid scan is suggested. To comparative evaluation of the proposed localization algorithm, the results of the localization algorithm based on multilateration in the same conditions are also provided. It is obtained that localization effect of proposed scheme is significantly better than localization scheme based on the multilateration for wireless channel loss model unknown environment. Furthermore, the weight factor τ in the constructed fitness function is also discussed. Overall, the simulation results have shown that the localization performance of PSO based on the mobile beacon is dependent upon the fitness function, the path planning of mobile beacon node.

In recent years node localization in wireless sensor networks (WSNs) has attracted much attention due to the increase of usage and applications of WSNs. Many algorithms and techniques for locating sensor nodes have been proposed in the literature. A recent algorithm, which is based on a given set of pairwise distance estimates among nodes and the target, generates a map of node locations. This algorithm, known as FastMap, uses projections onto orthogonal hyperplanes to find the coordinates successively. In this paper, we analytically study the performance of the FastMap algorithm for 2D positioning. Moreover a modified version of FastMap with better performance is proposed and analyzed. The analysis shows that the optimum anchor placement should be at the edge of the network.

Meta-heuristic-based guidance model for node localization in wireless sensor networks

Internet of Things (IOT) is experiencing a super-fast growing phase. Wireless Sensor Networks (WSN), as the “five sense organs” and teleneuron of IOT, is developing fast along. Owing to the application-oriented characteristics of WSN, WSN applications may differ enormously in implementation methods. The WSN developers may have to rewrite the code for each and every application. Along with the increasing demand to WSN applications, WSN developers may have heavier tasks of programming for these applications. Thanks to TinyOS and NesC, an operating system and programming language designed especially for WSN, raises the WSN application's programming to a higher level. Large number of supported sensor hardware plus system provided and user-de-fined libraries eases WSN application programmers' jobs. How-ever, the massive amount of nodes' communication and networking, and security protection slows down the development pace. A unified and empirical WSN application implementation platform is one of the available solutions to the problem. This paper introduces an empirical WSN application implementation platform, which avails the WSN developers build an application with unified code template and network packet generation and processing strategies.

Node localization algorithm for detecting malicious nodes to prevent connection failures and improve end-to-end delay

Within the growing Internet of Things (IoT) paradigm, a Wireless Sensor Network (WSN) is a critical component. In a WSN, sensor node localization is typically utilized to identify the target node’s current location at the sink node (SN). This allows local data to be analysed, making it more meaningful. However, there exists an intrinsic problem with node localization and energy efficiency, as identified in the literature, which has led to poor performance, namely, poor estimation, transmission, and detection of the network. This intrinsic problem also directly affects energy efficiency in a WSN, resulting in energy loss and poor node distribution in the WSN. There seems to be no lasting and reliable solution to this intrinsic node localization problem in WSNs. Hence, this research study proposed hyper-heuristic optimization algorithms to improve node localization and energy efficiency in WSNs. This research adopts the Design Research (DR) methodology and the Theory of Modelling and Simulation as the theoretical frameworks of the study. The hyper-heuristic model designed, was considered the conceptual framework of the study. To validate the novel technique, different sizes of sensor networks, namely: - 100 sensor nodes; 100 to 1 500 nodes and 200 to 450 sensor nodes with 20 anchor nodes were simulated in an area measuring 100m x 100m. The novel hyper-heuristic model was implemented in a MATLAB R2020a environment. The hyper-heuristic optimization algorithm’s substantial simulated experiment results were benchmarked utilizing state-of-the-art (modern) techniques to solve challenges related to node localization error, total energy consumed, average consumed packet energy, network throughput, shortest path, dead nodes, packets dispatched to the base station (BS), and the probability of error within the entire network dependent on size. The Data Energy Efficiency Clustering-Gaussian (DEEC-GAUSS) method was used to provide solutions to challenges related to energy efficiency in WSNs. In addition, this research study explored the use of the novel DEEC-GAUSS Gradient Distance Elimination Algorithm (DGGDEA) as the hyper-heuristic optimisation model for localization of nodes in WSNs. DEEC-GAUSS and DGGDEA were valuable additions to the body of knowledge. The results showed that the novel DEEC-GAUSS was the most energy efficient algorithm for 100 sensor nodes and 1000 to 1500 sensor nodes when compared to other stateof-the-art algorithms. Furthermore, the results showed that the novel DGGDEA was able to drastically minimize the node estimation error for sensor nodes. Reliability, accuracy and convergence using hyper-heuristic models to enhance the communication within WSNs has been simulated with evidence that DEEC-GAUSS and DGGDEA has outperformed other stateof-the-art approaches

Accurate and fast localization of randomly deployed sensor nodes is needed for many applications in wireless sensor networks. Localization also benefits in recognizing the geographically area where an event took place. There is no meaning of any event information without the knowledge of its location coordinates. DV-Hop is one of the main range free localization technique, which estimates the position of nodes using distance vector. Particle swarm optimization is suitable for the localization issues because of its fast computing speed and high precision. To further reduce the positioning error, the traditional DV-Hop localization algorithm based on single objective optimization algorithm is converted into a multi objective optimization algorithm. In our proposed scheme, we have considered six different single objective functions and three different multi objective functions. In this paper, a multi objective particle swarm optimization based DV-Hop localization is proposed in 3-dimensional wireless sensor networks. The proposed functions has been evaluated on the basis of computation time, average localization error and localization error variance. The simulation results show that our proposed multi objective function performs better as compared to traditional single objective function.

In many instances, as special applications of wireless sensor networks, wireless sensor networks need to know the location of nodes. A wireless sensor network localization algorithm based on Particle Swarm Optimization is proposed in this thesis to solve the problem of inaccurate positioning and large energy consumption for wireless sensor network node positioning. The algorithm combines the particle swarm optimization algorithm (PSO) and node localization algorithm to improve the positioning accuracy.

Resource Management Approach to an Efficient Wireless Sensor Network

A centralized three dimensional localization algorithm is proposed with the name of NMDS-MEEF, which is based on the location requirements of wireless sensor network used for monitoring large electrical equipment fault. The localization algorithm adopts a changed nonmetric multidimensional scaling in which the iterations process only makes use of the correlated data of one-hop neighbor nodes. The algorithm is more straightforward and practical. The simulation results demonstrate that the algorithm is of great advantages in improving localization accuracy, decreasing the dependence on the density of anchor nodes and reducing the influence by the accuracy of position estimation.