Error Minimization in Indoor Wireless Sensor Network Localization Using Genetic Technique

Using the genetic technique, error minimisation in indoor wireless sensor network localisation improves indoor wireless sensor network localisation during this field research. Sensor localisation-based techniques; several wireless device network applications require awareness of each node's physical location. The discovery of the position complete utilising range measurements also as sensor localisation received signal strength in time of arrival and sensor localisation received signal strength in a time difference of arrival and angle of arrival. WSN in positioning algorithms like the angle of arrival between two neighbour nodes. A wireless sensor network using positioning techniques in the area is assumed as localisation. WSNs always operate in an unattended manner, various situations like dynamic situations in the wireless network. It's impossible to exchange sensor manner after deployment. Therefore, a fundamental objective is to optimise the sensor manner lifetime. There has been much specialising in mobile sensor networks, and we have even seen the event of small-profile sensing devices that are ready to control their movement. Although it's been shown that mobility alleviates several issues regarding sensor network coverage and connectivity, many challenges remain node localisation in wireless device network is extremely important for several applications and received signal strength indicator has the capability of sensing, actuating the environmental data the actual-time and favourable information are often collected using the sensor in WSN systems. WSN is often combined with the internet of things to permit the association and extensive access to sensor data, and genetic techniques search the position of the nodes in WSN using all anchor nodes. A proposed algorithm as a genetic technique supported received signal strength, angle of arrival, receptive wireless device and also localisation wireless network. In the study, this paper problem that accuracy is low and error more, but the proposed algorithm overcomes this problem and minimises the error rate. Finally, the simplest possible location satisfies each factor with a minimal error rate and absolute best solution using GA

A survey on data storage and information discovery in the WSANs-based edge computing systems

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. In the post-Cloud era, the proliferation of Internet of Things (IoT) has pushed the horizon of Edge computing, which is a new computing paradigm with data are processed at the edge of the network. As the important systems of Edge computing, wireless sensor and actuator networks (WSANs) play an important role in collecting and processing the sensing data from the surrounding environment as well as taking actions on the events happening in the environment. In WSANs, in-network data storage and information discovery schemes with high energy efficiency, high load balance and low latency are needed because of the limited resources of the sensor nodes and the real-time requirement of some specific applications, such as putting out a big fire in a forest. In this article, the existing schemes of WSANs on data storage and information discovery are surveyed with detailed analysis on their advancements and shortcomings, and possible solutions are proposed on how to achieve high efficiency, good load balance, and perfect real-time performances at the same time, hoping that it can provide a good reference for the future research of the WSANs-based Edge computing systems

Routing, Localization And Positioning Protocols For Wireless Sensor And Actor Networks

Wireless sensor and actor networks (WSANs) are distributed systems of sensor nodes and actors that are interconnected over the wireless medium. Sensor nodes collect information about the physical world and transmit the data to actors by using one-hop or multi-hop communications. Actors collect information from the sensor nodes, process the information, take decisions and react to the events. This dissertation presents contributions to the methods of routing, localization and positioning in WSANs for practical applications. We first propose a routing protocol with service differentiation for WSANs with stationary nodes. In this setting, we also adapt a sports ranking algorithm to dynamically prioritize the events in the environment depending on the collected data. We extend this routing protocol for an application, in which sensor nodes float in a river to gather observations and actors are deployed at accessible points on the coastline. We develop a method with locally acting adaptive overlay network formation to organize the network with actor areas and to collect data by using locality-preserving communication. We also present a multi-hop localization approach for enriching the information collected from the river with the estimated locations of mobile sensor nodes without using positioning adapters. As an extension to this application, we model the movements of sensor nodes by a subsurface meandering current mobility model with random surface motion. Then we adapt the introduced routing and network organization methods to model a complete primate monitoring system. A novel spatial cut-off preferential attachment model and iii center of mass concept are developed according to the characteristics of the primate groups. We also present a role determination algorithm for primates, which uses the collection of spatial-temporal relationships. We apply a similar approach to human social networks to tackle the problem of automatic generation and organization of social networks by analyzing and assessing interaction data. The introduced routing and localization protocols in this dissertation are also extended with a novel three dimensional actor positioning strategy inspired by the molecular geometry. Extensive simulations are conducted in OPNET simulation tool for the performance evaluation of the proposed protocol

Permission-based fault tolerant mutual exclusion algorithm for mobile Ad Hoc networks

This study focuses on resolving the problem of mutual exclusion in mobile ad hoc networks. A Mobile Ad Hoc Network (MANET) is a wireless network without fixed infrastructure. Nodes are mobile and topology of MANET changes very frequently and unpredictably. Due to these limitations, conventional mutual exclusion algorithms presented for distributed systems (DS) are not applicable for MANETs unless they attach to a mechanism for dynamic changes in their topology. Algorithms for mutual exclusion in DS are categorized into two main classes including token-based and permission-based algorithms. Token-based algorithms depend on circulation of a specific message known as token. The owner of the token has priority for entering the critical section. Token may lose during communications, because of link failure or failure of token host. However, the processes for token-loss detection and token regeneration are very complicated and time-consuming. Token-based algorithms are generally non-fault-tolerant (although some mechanisms are utilized to increase their level of fault-tolerance) because of common problem of single token as a single point of failure. On the contrary, permission-based algorithms utilize the permission of multiple nodes to guarantee mutual exclusion. It yields to high traffic when number of nodes is high. Moreover, the number of message transmissions and energy consumption increase in MANET by increasing the number of mobile nodes accompanied in every decision making cycle. The purpose of this study is to introduce a method of managing the critical section,named as Ancestral, having higher fault-tolerance than token-based and fewer message transmissions and traffic rather that permission-based algorithms. This method makes a tradeoff between token-based and permission-based. It does not utilize any token, that is similar to permission-based, and the latest node having the critical section influences the entrance of the next node to the critical section, that is similar to token-based algorithms. The algorithm based on ancestral is named as DAD algorithms and increases the availability of fully connected network between 2.86 to 59.83% and decreases the number of message transmissions from 4j-2 to 3j messages (j as number of nodes in partition). This method is then utilized as the basis of dynamic ancestral mutual exclusion algorithm for MANET which is named as MDA. This algorithm is presented and evaluated for different scenarios of mobility of nodes, failure, load and number of nodes. The results of study show that MDA algorithm guarantees mutual exclusion,dead lock freedom and starvation freedom. It improves the availability of CS to minimum 154.94% and 113.36% for low load and high load of CS requests respectively compared to other permission-based lgorithm.Furthermore, it improves response time up to 90.69% for high load and 75.21% for low load of CS requests. It degrades the number of messages from n to 2 messages in the best case and from 3n/2 to n in the worst case. MDA algorithm is resilient to transient partitioning of network that is normally occurs due to failure of nodes or links

Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks

In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories and network formation to expedite data transmissions via multi-hop relaying. The trajectory planning aims to collect all GUs' data, while the UAVs' network formation optimizes the multi-hop UAV network topology to minimize the energy consumption and transmission delay. The joint network formation and trajectory optimization is solved by a two-step iterative approach. Firstly, we devise the adaptive network formation scheme by using a heuristic algorithm to balance the UAVs' energy consumption and data queue size. Then, with the fixed network formation, the UAVs' trajectories are further optimized by using multi-agent deep reinforcement learning without knowing the GUs' traffic demands and spatial distribution. To improve the learning efficiency, we further employ Bayesian optimization to estimate the UAVs' flying decisions based on historical trajectory points. This helps avoid inefficient action explorations and improves the convergence rate in the model training. The simulation results reveal close spatial-temporal couplings between the UAVs' trajectory planning and network formation. Compared with several baselines, our solution can better exploit the UAVs' cooperation in data offloading, thus improving energy efficiency and delay performance.Comment: 15 pages, 10 figures, 2 algorithm

Reinforcement and deep reinforcement learning for wireless Internet of Things: A survey

International audienceNowadays, many research studies and industrial investigations have allowed the integration of the Internet of Things (IoT) in current and future networking applications by deploying a diversity of wireless-enabled devices ranging from smartphones, wearables, to sensors, drones, and connected vehicles. The growing number of IoT devices, the increasing complexity of IoT systems, and the large volume of generated data have made the monitoring and management of these networks extremely difficult. Numerous research papers have applied Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) techniques to overcome these difficulties by building IoT systems with effective and dynamic decision-making mechanisms, dealing with incomplete information related to their environments. The paper first reviews pre-existing surveys covering the application of RL and DRL techniques in IoT communication technologies and networking. The paper then analyzes the research papers that apply these techniques in wireless IoT to resolve issues related to routing, scheduling, resource allocation, dynamic spectrum access, energy, mobility, and caching. Finally, a discussion of the proposed approaches and their limits is followed by the identification of open issues to establish grounds for future research directions proposal