Wireless Sensor Networks and Multi-UAV systems for natural disaster management

The book reviews section generally accepts for review only those books whose content and level reflect the general editorial policy of Technometrics. Publishers are invited to send books for review to Eric R. Ziegel, Amoco Research Center, Mail Station F-l/C& P.O. Box 3011, Naperville, Illinois 60566-7011. Please include the price of the book. The opinions expressed in this section are those of the reviewers and do not necessarily reflect those of the editorial staff or the sponsoring societies. Listed prices were provided by the publisher when the books were received by Technometrics and may not be current.

Adventures in Stochastic Processes

Disastrous events are one of the most challenging applications of multihop ad hoc networks due to possible damages of existing telecommunication infrastructure. The deployed cellular communication infrastructure might be partially or completely destroyed after a natural disaster. Multihop ad hoc communication is an interesting alternative to deal with the lack of communications in disaster scenarios. They have evolved since their origin, leading to different ad hoc paradigms such as MANETs, VANETs, DTNs, or WSNs. This paper presents a survey on multihop ad hoc network paradigms for disaster scenarios. It highlights their applicability to important tasks in disaster relief operations. More specifically, the paper reviews the main work found in the literature, which employed ad hoc networks in disaster scenarios. In addition, it discusses the open challenges and the future research directions for each different ad hoc paradigm.

A survey on multihop ad hoc networks for disaster response scenarios

Micro aerial vehicles (MAVs) provide data such as images and videos from an aerial perspective, with data typically transferred to the ground. To establish connectivity in larger areas, a fleet of MAVs may set up an ad-hoc wireless network. Packet forwarding in aerial networks is challenged by unstable link quality and intermittent connectivity caused by MAV movement. We show that signal obstruction by the MAV frame can be alleviated by adapting the MAV platform, even for low-priced MAVs, and the aerial link can be properly characterized by its geographical distance. Based on this link characterization and making use of GPS and inertial sensors on-board of MAVs, we design and implement a motion-driven packet forwarding algorithm. The algorithm unites location-aware end-to-end routing and delay-tolerant forwarding, extended by two predictive heuristics. Given the current location, speed, and orientation of the MAVs, future locations are estimated and used to refine packet forwarding decisions. We study the forwarding algorithm in a field measurement campaign with quadcopters connected over Wi-Fi IEEE 802.11n, complemented by simulation. Our analysis confirms that the proposed algorithm masters intermittent connectivity well, but also discloses inefficiencies of location-aware forwarding. By anticipating motion, such inefficiencies can be counteracted and the forwarding performance can be improved.

Route or Carry: Motion-Driven Packet Forwarding in Micro Aerial Vehicle Networks

Floods are amongst the most common and devastating of all natural hazards. The alarming number of flood-related deaths and financial losses suffered annually across the world call for improved response to flood risks. Interestingly, the last decade has presented great opportunities with a series of scholarly activities exploring how camera images and wireless sensor data from Internet-of-Things (IoT) networks can improve flood management. This paper presents a systematic review of the literature regarding IoT-based sensors and computer vision applications in flood monitoring and mapping. The paper contributes by highlighting the main computer vision techniques and IoT sensor approaches utilised in the literature for real-time flood monitoring, flood modelling, mapping and early warning systems including the estimation of water level. The paper further contributes by providing recommendations for future research. In particular, the study recommends ways in which computer vision and IoT sensor techniques can be harnessed to better monitor and manage coastal lagoons—an aspect that is under-explored in the literature.

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Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review.

Large scale disasters like the earthquake and tsunami in Japan (2011) cripple the local infrastructure. Proprietary systems and protocols used today for disaster response still lack data at the high spatial and temporal resolution needed to quickly save lives and to support disaster recovery efforts. Victims are rescued after days, if not weeks; digital coordination interfaces among responders are lacking, or are based on archaic methods (pencil, paper, paint on walls); the delay in receiving vast amounts of information from the field is bounded by the time used to physically transport tapes or hard drives. In this paper we present the design, implementation and evaluation of DistressNet, a system that provides services for emergency response applications. DistressNet integrates a variety of rapidly deployable, battery powered COTS devices into a secure framework. An optimal placement of networked components allows users to quickly and reliably store and retrieve data, in a ''cloud''-like manner, from a local intermittently connected ''fog''. High volumes of field data are available for emergency response personnel to view on interfaces like smartphones and tablets. DistressNet is a large academic effort, proposing open systems, instead of proprietary solutions. It has been developed in collaboration with Texas Task Force 1 and its components have been evaluated for over one year in outdoor deployments that required over 1500 man hours.

http://foresight.ifmo.ru/ict/shared/files/201312/1_198.pdf

DistressNet: A disaster response system providing constant availability cloud-like services

Time is a critical factor in the Urban Search & Rescue operations immediately following natural and man-made disasters. Building on our collaboration with first responders we identify a set of areas for improving response times: victim detection in collapsed buildings, information storage and collection about buildings (collapsed or not), detection of first responder team separation and lost tools, and throughput and latency of data delivered to first responders. In this paper, we present the design (i.e., software/hardware architectures, and the guiding design principles), implementation and realistic evaluation of DistressNet, a system that targets the aforementioned areas for reducing the Urban Search & Rescue response time. DistressNet, built on COTS hardware and on open standards and protocols, pushes complexity that the very diverse Urban Search & Rescue scenarios pose, to user level applications (apps). Apps in DistressNet run on unmodified hardware ranging from smartphones, to motes and wireless routers. For the benefit of the research community, we also share some lessons learned during our experiences in the design, building and evaluation of DistressNet.

A wireless system for reducing response time in Urban Search & Rescue

Monitoring flow-based systems (FBS) (e.g., water distribution systems, oil and gas pipelines, the human cardiovascular system) is of paramount importance considering their economic and health impacts. FBS monitoring typically has been achieved by costly, complex, static sensors that are strategically placed. To reduce the cost of monitoring, we propose a mobile wireless sensor network (WSN) system comprised of mobile sensors (their movement aided by the inherent flow in the FBS) and static beacons that aid in locating sensors. This article presents the first complete architectural design, algorithms, and protocols for optimal monitoring of FBS. Our proposed solution includes sensing and communication models, MAC and group management protocols for sensor and beacon communication, and algorithms for sensor and beacon placement. We compare our proposed solution with the state of the art through extensive simulations and a proof-of-concept system implementation. We demonstrate performance improvements, such as a dramatic reduction (a factor of 91) in the number of sensors when the sensing range is marginally (2.5 times) increased.

Toward Optimal Monitoring of Flow-Based Systems Using Mobile Wireless Sensor Networks

Network coding and duty-cycling are two major techniques for saving energy in wireless sensor networks. To the best of our knowledge, the idea to combine these two techniques for even more aggressive energy savings, has not been explored. This is not unusual, since these two techniques achieve energy efficiency through conflicting means, e.g., network coding saves energy by exploiting overhearing (i.e., nodes are awake), whereas duty-cycling saves energy by reducing idle listening (i.e., nodes sleep). In this article, we thoroughly investigate if network coding and duty cycling can be used together for more aggressive energy savings in flood-based wireless sensor networks. Our main idea is to exploit the redundancy sometimes present in flooding applications that use network coding, and put a node to sleep (i.e., duty cycle) when a redundant transmission takes place (i.e., the node has already received and successfully decoded a sequence of network-coded packets). We propose a scheme, called DutyCode, in which a multiple access control (MAC) protocol implements packet streaming and allows the network coding-aware application to decide when a node can sleep. We also present an algorithm for deciding the optimal coding scheme for a node to further reduce energy consumption by minimizing redundant packet transmissions. Finally, we propose an adaptive switching technique between DutyCode and an existing duty-cycling MAC protocol. We investigate our proposed solutions analytically and implement them on mote hardware. Our performance evaluation results, obtained from a 42-node indoor testbed, show that our scheme saves 30-46% more energy than network coding-based solutions.

On combining network coding with duty-cycling in flood-based wireless sensor networks

Geographic routing is well suited for large scale sensor networks, because its per node state is independent of the network size. However, the local minimum caused by holes/obstacles results in the worst-case path stretch of @W(c^2), where c is the path length of the optimal route. Recently, a geographic routing protocol based on the visibility graph (VIGOR) showed that a path stretch of @Q(c) can be achieved. This path stretch, however, is achieved at the cost of communication and storage overhead, which makes the practical deployment of VIGOR in large scale sensor networks challenging. To this end, we propose GOAL (Geometric Routing using Abstracted Holes), a routing protocol that provably achieves a path stretch of @Q(c), with lower communication and storage overhead. To compactly describe holes, we develop a novel distributed convex hull algorithm, which improves the message complexity O(n log^2n) of state of art distributed convex hull algorithm to O(n log n). The concise representation of a hole is used by nodes to make locally optimal routing decisions. Our theoretical analysis proves the correctness of the proposed algorithms and the path stretch of @Q(c). Through extensive simulations and experiments on a testbed with 42 EPIC motes, we demonstrate the effectiveness of GOAL and its feasibility for resource constrained wireless sensor networks; specifically, we show that GOAL eliminates part of communication overhead of VIGOR and reduces the memory overhead of VIGOR by up to 51%.

Wei Zhang

Papers

DistressNet: A disaster response system providing constant availability cloud-like services

A wireless system for reducing response time in Urban Search & Rescue

Toward Optimal Monitoring of Flow-Based Systems Using Mobile Wireless Sensor Networks

On combining network coding with duty-cycling in flood-based wireless sensor networks

GOAL: A parsimonious geographic routing protocol for large scale sensor networks