The Role of the Internet of Things in Network Resilience

Disasters lead to devastating structural damage not only to buildings and transport infrastructure, but also to other critical infrastructure, such as the power grid and communication backbones. Following such an event, the availability of minimal communication services is however crucial to allow efficient and coordinated disaster response, to enable timely public information, or to provide individuals in need with a default mechanism to post emergency messages. The Internet of Things consists in the massive deployment of heterogeneous devices, most of which battery-powered, and interconnected via wireless network interfaces. Typical IoT communication architectures enables such IoT devices to not only connect to the communication backbone (i.e. the Internet) using an infrastructure-based wireless network paradigm, but also to communicate with one another autonomously, without the help of any infrastructure, using a spontaneous wireless network paradigm. In this paper, we argue that the vast deployment of IoT-enabled devices could bring benefits in terms of data network resilience in face of disaster. Leveraging their spontaneous wireless networking capabilities, IoT devices could enable minimal communication services (e.g. emergency micro-message delivery) while the conventional communication infrastructure is out of service. We identify the main challenges that must be addressed in order to realize this potential in practice. These challenges concern various technical aspects, including physical connectivity requirements, network protocol stack enhancements, data traffic prioritization schemes, as well as social and political aspects

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

Establishing effective communications in disaster affected areas and artificial intelligence based detection using social media platform

Floods, earthquakes, storm surges and other natural disasters severely affect the communication infrastructure and thus compromise the effectiveness of communications dependent rescue and warning services. In this paper, a user centric approach is proposed to establish communications in disaster affected and communication outage areas. The proposed scheme forms ad hoc clusters to facilitate emergency communications and connect end-users/ User Equipment (UE) to the core network. A novel cluster formation with single and multi-hop communication framework is proposed. The overall throughput in the formed clusters is maximized using convex optimization. In addition, an intelligent system is designed to label different clusters and their localities into affected and non-affected areas. As a proof of concept, the labeling is achieved on flooding dataset where region specific social media information is used in proposed machine learning techniques to classify the disaster-prone areas as flooded or unflooded. The suitable results of the proposed machine learning schemes suggest its use along with proposed clustering techniques to revive communications in disaster affected areas and to classify the impact of disaster for different locations in disaster-prone areas

Ten years of cooperation between mobile robots and sensor networks

This paper presents an overview of the work carried out by the Group of Robotics, Vision and Control (GRVC) at the University of Seville on the cooperation between mobile robots and sensor networks. The GRVC, led by Professor Anibal Ollero, has been working over the last ten years on techniques where robots and sensor networks exploit synergies and collaborate tightly, developing numerous research projects on the topic. In this paper, based on our research, we introduce what we consider some relevant challenges when combining sensor networks with mobile robots. Then, we describe our developed techniques and main results for these challenges. In particular, the paper focuses on autonomous self-deployment of sensor networks; cooperative localization and tracking; self-localization and mapping; and large-scale scenarios. Extensive experimental results and lessons learnt are also discussed in the paper