Distributed Recognition of Reference Nodes for Wireless Sensor Network Localization

All known localization techniques for wireless sensor and ad-hoc networks require certain set of reference nodes being used for position estimation. The anchor-free techniques in contrast to anchor-based do not require reference nodes called anchors to be placed in the network area before localization operation itself, but they can establish own reference coordinate system to be used for the relative position estimation. We observed that contemporary anchor-free localization algorithms achieve a low localization error, but dissipate significant energy reserves during the recognition of reference nodes used for the position estimation. Therefore, we have proposed the optimized anchor-free localization algorithm referred to as BRL (Boundary Recognition aided Localization), which achieves a low localization error and mainly reduces the communication cost of the reference nodes recognition phase. The proposed BRL algorithm was investigated throughout the extensive simulations on the database of networks with the different number of nodes and densities and was compared in terms of communication cost and localization error with the known related algorithms such as AFL and CRP. Through the extensive simulations we have observed network conditions where novel BRL algorithm excels in comparison with the state of art

On the design of smart parking networks in the smart cities: an optimal sensor placement model

Smart parking is a typical IoT application that can benefit from advances in sensor, actuator and RFID technologies to provide many services to its users and parking owners of a smart city. This paper considers a smart parking infrastructure where sensors are laid down on the parking spots to detect car presence and RFID readers are embedded into parking gates to identify cars and help in the billing of the smart parking. Both types of devices are endowed with wired and wireless communication capabilities for reporting to a gateway where the situation recognition is performed. The sensor devices are tasked to play one of the three roles: (1) slave sensor nodes located on the parking spot to detect car presence/absence; (2) master nodes located at one of the edges of a parking lot to detect presence and collect the sensor readings from the slave nodes; and (3) repeater sensor nodes, also called ''anchor'' nodes, located strategically at specific locations in the parking lot to increase the coverage and connectivity of the wireless sensor network. While slave and master nodes are placed based on geographic constraints, the optimal placement of the relay/anchor sensor nodes in smart parking is an important parameter upon which the cost and e ciency of the parking system depends. We formulate the optimal placement of sensors in smart parking as an integer linear programming multi-objective problem optimizing the sensor network engineering e ciency in terms of coverage and lifetime maximization, as well as its economic gain in terms of the number of sensors deployed for a specific coverage and lifetime. We propose an exact solution to the node placement problem using single-step and two-step solutions implemented in the Mosel language based on the Xpress-MPsuite of libraries. Experimental results reveal the relative e ciency of the single-step compared to the two-step model on di erent performance parameters. These results are consolidated by simulation results, which reveal that our solution outperforms a random placement in terms of both energy consumption, delay and throughput achieved by a smart parking network

Dynamic mobile anchor path planning for underwater wireless sensor networks

In an underwater wireless sensor network (UWSN), the location of the sensor nodes plays a significant role in the localization process. The location information is obtained by using the known positions of anchor nodes. For underwater environments, instead of using various static anchor nodes, mobile anchor nodes are more efficient and cost-effective to cover the monitoring area. Nevertheless, the utilization of these mobile anchors requires adequate path planning strategy. Mzost of the path planning algorithms do not consider irregular deployment, caused by the effects of water currents. Consequently, this leads towards the inefficient energy consumption by mobile anchors due to unnecessary transmission of beacon messages at unnecessary areas. Therefore, an efficient dynamic mobile path planning (EDMPP) algorithm to tackle the irregular deployment and non-collinear virtual beacon point placement, targeting the underwater environment settings is presented in this paper. In addition, EDMPP controls the redundant beacon message deployment and overlapping, for beacon message distribution in mobile assistant localization. The simulation results show that the performance of the EDMPP is improved by increasing the localization accuracy and decreasing the energy consumption with optimum path length

Enabling Cyber Physical Systems with Wireless Sensor Networking Technologies

[[abstract]]Over the last few years, we have witnessed a growing interest in Cyber Physical Systems (CPSs) that rely on a strong synergy between computational and physical components. CPSs are expected to have a tremendous impact on many critical sectors (such as energy, manufacturing, healthcare, transportation, aerospace, etc) of the economy. CPSs have the ability to transform the way human-to-human, human-toobject, and object-to-object interactions take place in the physical and virtual worlds. The increasing pervasiveness of Wireless Sensor Networking (WSN) technologies in many applications make them an important component of emerging CPS designs. We present some of the most important design requirements of CPS architectures. We discuss key sensor network characteristics that can be leveraged in CPS designs. In addition, we also review a few well-known CPS application domains that depend on WSNs in their design architectures and implementations. Finally, we present some of the challenges that still need to be addressed to enable seamless integration of WSN with CPS designs.[[incitationindex]]SCI[[booktype]]紙

Location Management in IP-based Future LEO Satellite Networks: A Review

Future integrated terrestrial, aerial, and space networks will involve thousands of Low Earth Orbit (LEO) satellites forming a network of mega-constellations, which will play a significant role in providing communication and Internet services everywhere, at any time, and for everything. Due to its very large scale and highly dynamic nature, future LEO satellite networks (SatNets) management is a very complicated and crucial process, especially the mobility management aspect and its two components location management and handover management. In this article, we present a comprehensive and critical review of the state-of-the-art research in LEO SatNets location management. First, we give an overview of the Internet Engineering Task Force (IETF) mobility management standards (e.g., Mobile IPv6 and Proxy Mobile IPv6) and discuss their location management techniques limitations in the environment of future LEO SatNets. We highlight future LEO SatNets mobility characteristics and their challenging features and describe two unprecedented future location management scenarios. A taxonomy of the available location management solutions for LEO SatNets is presented, where the solutions are classified into three approaches. The "Issues to consider" section draws attention to critical points related to each of the reviewed approaches that should be considered in future LEO SatNets location management. To identify the gaps, the current state of LEO SatNets location management is summarized. Noteworthy future research directions are recommended. This article is providing a road map for researchers and industry to shape the future of LEO SatNets location management.Comment: Submitted to the Proceedings of the IEE

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

An Effective PSO-Based Node Localization Scheme for Wireless Sensor Networks

[[abstract]]Wireless sensor networks (WSNs) usually employ different ranging techniques to measure the distance between an unknown node and its neighboring anchor nodes, and based on the measured distance to estimate the position of the unknown node. This paper presents an effective Particle Swarm Optimization (PSO)-based Localization Scheme using the Radio Signal Strength (RSS) ranging technique. Modified from the iterative multilateration algorithm, our scheme is unique in adopting the location data of remote anchors provided by the closest neighbor anchors of an unknown node to estimate the unknown nodepsilas position and using the PSO algorithm to further reduce error accumulation. The new scheme meanwhile takes in a modified DV-distance approach to raise the success ratios of locating unknown nodes. Compared with related schemes, our scheme is shown through simulations to perform constantly better in increasing localization success ratios and decreasing location errors -- at reduced cost.[[conferencetype]]國際[[conferencedate]]20081201~20081204[[iscallforpapers]]Y[[conferencelocation]]Dunedin , New Zealan