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

A Centralized Energy Management System for Wireless Sensor Networks

This document presents the Centralized Energy Management System (CEMS), a dynamic fault-tolerant reclustering protocol for wireless sensor networks. CEMS reconfigures a homogeneous network both periodically and in response to critical events (e.g. cluster head death). A global TDMA schedule prevents costly retransmissions due to collision, and a genetic algorithm running on the base station computes cluster assignments in concert with a head selection algorithm. CEMS\u27 performance is compared to the LEACH-C protocol in both normal and failure-prone conditions, with an emphasis on each protocol\u27s ability to recover from unexpected loss of cluster heads

PhyNetLab: An IoT-Based Warehouse Testbed

Future warehouses will be made of modular embedded entities with communication ability and energy aware operation attached to the traditional materials handling and warehousing objects. This advancement is mainly to fulfill the flexibility and scalability needs of the emerging warehouses. However, it leads to a new layer of complexity during development and evaluation of such systems due to the multidisciplinarity in logistics, embedded systems, and wireless communications. Although each discipline provides theoretical approaches and simulations for these tasks, many issues are often discovered in a real deployment of the full system. In this paper we introduce PhyNetLab as a real scale warehouse testbed made of cyber physical objects (PhyNodes) developed for this type of application. The presented platform provides a possibility to check the industrial requirement of an IoT-based warehouse in addition to the typical wireless sensor networks tests. We describe the hardware and software components of the nodes in addition to the overall structure of the testbed. Finally, we will demonstrate the advantages of the testbed by evaluating the performance of the ETSI compliant radio channel access procedure for an IoT warehouse

Evaluation and Analysis for Maximum Lifespan of Wireless Sensor Networks by Energy-Efficient Design

Wireless Sensor Networks (WSNs) have used worldwide in the past few years and are now being used in health monitoring ,disaster management, defense, telecommunications, etc. Such networks are used in many industrial and consumer applications such as industrial process and environment monitoring, among others. A WSN network is a collection of specialized transducers known as sensor nodes with a communication link distributed randomly in any locations to monitor environmental parameters such as water level, and temperature. Each sensor node is equipped with a transducer, a signal processor, a power unit, and a transceiver. WSNs are now being widely used to monitor environmental parameters, including the amount of gas, water, temperature, humidity, oxygen level, dust, etc. The WSN for environment monitoring can be equivalently replaced by a multiple-input multiple-output (MIMO) relay network. Multi-hop relay networks have attracted significant research interest in recent years for their capability in increasing the coverage range. The network communication link from a source to a destination is implemented using the amplify-and-forward (AF) or decode-and-forward (DF) schemes. The AF relay receives information from the previous relay and simply amplifies the received signal and then forwards it to the next relay. On the other hand, the DF relay first decodes the received signal and then forwards it to the next relay in the second stage if it can perfectly decode the incoming signal. For analytical simplicity, in this thesis, we consider the AF relaying scheme and the results of this work can also be developed for the DF relay