Search and rescue (SAR) operations can take significant advantage from supporting autonomous or teleoperated robots and multi-robot systems. These can aid in mapping and situational assessment, monitoring and surveillance, establishing communication networks, or searching for victims. This paper provides a review of multi-robot systems supporting SAR operations, with system-level considerations and focusing on the algorithmic perspectives for multi-robot coordination and perception. This is, to the best of our knowledge, the first survey paper to cover (i) heterogeneous SAR robots in different environments, (ii) active perception in multi-robot systems, while (iii) giving two complementary points of view from the multi-agent perception and control perspectives. We also discuss the most significant open research questions: shared autonomy, sim-to-real transferability of existing methods, awareness of victims’ conditions, coordination and interoperability in heterogeneous multi-robot systems, and active perception. The different topics in the survey are put in the context of the different challenges and constraints that various types of robots (ground, aerial, surface, or underwater) encounter in different SAR environments (maritime, urban, wilderness, or other post-disaster scenarios). The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area.

Increased automation and intelligence in computer systems have revealed limitations of Cloud-based computing such as unpredicted latency in safety-critical and performance-sensitive applications. The amount of data generated from ubiquitous sensors has reached a degree where it becomes impractical to always store and process in the Cloud. Edge computing brings computation and storage to the Edge of the network near to where the data originates yielding reduced network load and better performance of services. In parallel, new wireless communication technologies have appeared to facilitate the expansion of Internet of Things (IoT). Instead of seeking higher data rates, low-power wide-area network aims at battery-powered sensor nodes and devices which require reliable communication for a prolonged period of time. Recently, Long Range (LoRa) has become a popular choice for IoT-based solutions. In this paper, we explore and analyze different application fields and related works which use LoRa and investigate potential improvement opportunities and considerations. Furthermore, we propose a generic architecture to integrate Edge computation capability in IoT-based applications for enhanced performance.

A rapidly growing number of vehicles in recent years cause long traffic jams and difficulty in the management of traffic in cities. One of the most significant reasons for increased traffic jams on the road is random parking in unauthorized and non-permitted places. In addition, managing of available parking places cannot achieve the expected reduction in traffic congestion related problems due to mismanagement, lack of real-time parking guidance to the drivers, and general ignorance. As the number of roads, highways and related resources has not increased significantly, a rising need for a smart, dynamic and effective parking solution is observed. Accordingly, with the use of multiple sensors, appropriate communication network and advanced processing capabilities of edge and cloud computing, a smart parking system can help manage parking effectively and make it easier for the vehicle owners. In this paper, we propose a multi-layer architecture for smart parking system consisting of multi-parametric parking slot sensor nodes, latest long-range low-power wireless communication technology and Edge-Cloud computation. The proposed system enables dynamic management of parking for large areas while providing useful information to the drivers about available parking locations and related services through near real-time monitoring of vehicles. Furthermore, we propose a dynamic pricing algorithm to yield maximum possible revenue for the parking authority and optimum parking slot availability for the drivers.

Indoor mobile robots are widely used in industrial environments such as large logistic warehouses. They are often in charge of collecting or sorting products. For such robots, computation-intensive operations account for a significant percentage of the total energy consumption and consequently affect battery life. Besides, in order to keep both the power consumption and hardware complexity low, simple micro-controllers or single-board computers are used as onboard local control units. This limits the computational capabilities of robots and consequently their performance. Offloading heavy computation to Cloud servers has been a widely used approach to solve this problem for cases where large amounts of sensor data such as real-time video feeds need to be analyzed. More recently, Fog and Edge computing are being leveraged for offloading tasks such as image processing and complex navigation algorithms involving non-linear mathematical operations. In this paper, we present a system architecture for offloading computationally expensive localization and mapping tasks to smart Edge gateways which use Fog services. We show how Edge computing brings computational capabilities of the Cloud to the robot environment without compromising operational reliability due to connection issues. Furthermore, we analyze the power consumption of a prototype robot vehicle in different modes and show how battery life can be significantly improved by moving the processing of data to the Edge layer.

A better lifestyle starts with a healthy heart. Unfortunately, millions of people around the world are either directly affected by heart diseases such as coronary artery disease and heart muscle disease (Cardiomyopathy), or are indirectly having heart-related problems like heart attack and/or heart rate irregularity. Monitoring and analyzing these heart conditions in some cases could save a life if proper actions are taken accordingly. A widely used method to monitor these heart conditions is to use ECG or electrocardiography. However, devices used for ECG are costly, energy inefficient, bulky, and mostly limited to the ambulatory environment. With the advancement and higher affordability of Internet of Things (IoT), it is possible to establish better health-care by providing real-time monitoring and analysis of ECG. In this paper, we present a low-cost health monitoring system that provides continuous remote monitoring of ECG together with automatic analysis and notification. The system consists of energy-efficient sensor nodes and a fog layer altogether taking advantage of IoT. The sensor nodes collect and wirelessly transmit ECG, respiration rate, and body temperature to a smart gateway which can be accessed by appropriate care-givers. In addition, the system can represent the collected data in useful ways, perform automatic decision making and provide many advanced services such as real-time notifications for immediate attention.