Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Optimal Route Planning with Mobile Nodes in Wireless Sensor Networks

Wireless Sensor Networks (WSN) are a collection of sensor nodes that sense their surroundings and relay their proximal information for further analysis. They utilize wireless communication technology to allow monitoring areas remotely. A major problem with WSNs is that the sensor nodes have a set sensing radius, which may not cover the entire field space. This issue would lead to an unreliable WSN that sometimes would not discover or report about events taking place in the field space. Researchers have focused on developing techniques for improving area coverage. These include allowing mobile sensor nodes to dynamically move towards coverage holes through the use of a path planning approach to solve issues such as maximizing area coverage. An approach is proposed in this thesis to maximize the area of network coverage by the WSN through a Mixed Integer Linear Programming (MILP) formulation which utilizes both static and mobile nodes. The mobile nodes are capable of travelling across the area of interest, to cover empty ‘holes’ (i.e. regions not covered by any of the static nodes) in a WSN. The goal is to find successive positions of the mobile node through the network, in order to maximize the network area coverage, or achieve a specified level of coverage while minimizing the number of iterations taken. Simulations of the formulation on small WSNs show promising results in terms of both objectives

Optimal fault-tolerant placement of relay nodes in a mission critical wireless network

The operations of many critical infrastructures (e.g., airports) heavily depend on proper functioning of the radio communication network supporting operations. As a result, such a communication network is indeed a mission-critical communication network that needs adequate protection from external electromagnetic interferences. This is usually done through radiogoniometers. Basically, by using at least three suitably deployed radiogoniometers and a gateway gathering information from them, sources of electromagnetic emissions that are not supposed to be present in the monitored area can be localised. Typically, relay nodes are used to connect radiogoniometers to the gateway. As a result, some degree of fault-tolerance for the network of relay nodes is essential in order to offer a reliable monitoring. On the other hand, deployment of relay nodes is typically quite expensive. As a result, we have two conflicting requirements: minimise costs while guaranteeing a given fault-tolerance. In this paper address the problem of computing a deployment for relay nodes that minimises the relay node network cost while at the same time guaranteeing proper working of the network even when some of the relay nodes (up to a given maximum number) become faulty (fault-tolerance). We show that the above problem can be formulated as a Mixed Integer Linear Programming (MILP) as well as a Pseudo-Boolean Satisfiability (PB-SAT) optimisation problem and present experimental results com- paring the two approaches on realistic scenarios

INVESTIGATION ON ENERGY BASED DATA GATHERING APPROACH FOR WSN

Wireless Sensor Networks plays a vital role in all emerging areas of Wireless Platforms like Interne of Things (IoT), WiFi, WiMAX etc. Sensor nodes are communicated with or without the presence of administrator. Data gathering is a major issue in WSN which influences the throughput, energy and data delivery. In previous research, there was not taken efforts to focus on balanced data gathering.  In this research, we propose Reliable Energy Efficient Data Gathering Approach (REEDGA) to balance data gathering and overhead. To achieve this, proposed work consists of three phases. In first phase, estimation of information gathering is implemented through stable paths. Stable paths are found based on link cost. In second phase, data gathering phase is initialized to save energy in the presence of mobile sensor nodes. Overhead is kept low while keeping round trip time of gathered data. From the analytical simulation using NS2, the proposed approach achieves better performance in terms of data delivery rate, data gathering rate, throughput, delay, link availability and control overhead

An Energy Efficient Data Collection Using Multiple UAVs in Wireless Sensor Network: A Survey Study

   اليوم، مع التقدم العلمي والتكنولوجي في الروبوتات، والذكاء الاصطناعي، والسيطرة والحواسيب، المركبات البرية والجوية والبحرية قد تم الاهتمام بها. كما تم تحسين الطائرات بدون طيار (UAVs) بشكل كبير وهي مفيدة جدا للعديد من التطبيقات الهامة في الأعمال التجارية والبيئة الحضرية والعسكرية. أحد أهم استخدامات الطائرات بدون طيار في شبكات الاستشعار اللاسلكية (WSNs)  التي لديها طاقة منخفضة وقد لا تكون قادرة على الاتصال في مناطق واسعة. في هذه الحالة ، يمكن أن توفر الطائرة بدون طيار وسيلة لجمع بيانات WSN من جهاز واحد ونقلها إلى المستلم المقصود تركز هذه المقالة على مجال البحث في التطبيقات العملية للطائرات بدون طيار كجامع متنقل لشبكات الاستشعار اللاسلكية. أولا التحقيقات حول الطائرات بدون طيار المقترحة تم دراستها ومقارنة نقاط ضعفها مع بعضها البعض. وكذلك التحديات التقنية لتطبيقات الطائرات بدون طيار في شبكة الاستشعار اللاسلكية تم استكشافها.Today, with scientific and technological advances in robotics, artificial intelligence, control and computers, land, air, and sea vehicles, they have been considered. Unmanned aerial vehicles (UAVs) have also significantly improved and are very useful for many important applications in the business, urban and military environment. One of the important uses of UAVs in Wireless Sensor Networks (WSNs) is that devices with low energy and may not be able to communicate in large areas. Nevertheless, a UAV can provide a tool for collecting the data of WSN from one device and transmitting it to another device. This article focuses on the field of research on practical applications of UAVs as mobile collectors for wireless sensor networks. First, the investigations of the proposed UAV were studied and compared their weaknesses with each other. Then, the technical challenges of the applications of UAVs in the wireless sensor network were explored

Efficient approach for maximizing lifespan in wireless sensor networks by using mobile sinks

Recently, sink mobility has been shown to be highly beneficial in improving network lifetime in wireless sensor networks (WSNs). Numerous studies have exploited mobile sinks (MSs) to collect sensed data in order to improve energy efficiency and reduce WSN operational costs. However, there have been few studies on the effectiveness of MS operation on WSN closed operating cycles. Therefore, it is important to investigate how data is collected and how to plan the trajectory of the MS in order to gather data in time, reduce energy consumption, and improve WSN network lifetime. In this study, we combine two methods, the cluster-head election algorithm and the MS trajectory optimization algorithm, to propose the optimal MS movement strategy. This study aims to provide a closed operating cycle for WSNs, by which the energy consumption and running time of a WSN is minimized during the cluster election and data gathering periods. Furthermore, our flexible MS movement scenarios achieve both a long network lifetime and an optimal MS schedule. The simulation results demonstrate that our proposed algorithm achieves better performance than other well-known algorithms