Routing Protocols for Underwater Acoustic Sensor Networks: A Survey from an Application Perspective

Underwater acoustic communications are different from terrestrial radio communications; acoustic channel is asymmetric and has large and variable end‐to‐end propagation delays, distance‐dependent limited bandwidth, high bit error rates, and multi‐path fading. Besides, nodes’ mobility and limited battery power also cause problems for networking protocol design. Among them, routing in underwater acoustic networks is a challenging task, and many protocols have been proposed. In this chapter, we first classify the routing protocols according to application scenarios, which are classified according to the number of sinks that an underwater acoustic sensor network (UASN) may use, namely single‐sink, multi‐sink, and no‐sink. We review some typical routing strategies proposed for these application scenarios, such as cross‐layer and reinforcement learning as well as opportunistic routing. Finally, some remaining key issues are highlighted

Energy optimization for wireless sensor networks using hierarchical routing techniques

Philosophiae Doctor - PhDWireless sensor networks (WSNs) have become a popular research area that is widely gaining the attraction from both the research and the practitioner communities due to their wide area of applications. These applications include real-time sensing for audio delivery, imaging, video streaming, and remote monitoring with positive impact in many fields such as precision agriculture, ubiquitous healthcare, environment protection, smart cities and many other fields. While WSNs are aimed to constantly handle more intricate functions such as intelligent computation, automatic transmissions, and in-network processing, such capabilities are constrained by their limited processing capability and memory footprint as well as the need for the sensor batteries to be cautiously consumed in order to extend their lifetime. This thesis revisits the issue of the energy efficiency in sensor networks by proposing a novel clustering approach for routing the sensor readings in wireless sensor networks. The main contribution of this dissertation is to 1) propose corrective measures to the traditional energy model adopted in current sensor networks simulations that erroneously discount both the role played by each node, the sensor node capability and fabric and 2) apply these measures to a novel hierarchical routing architecture aiming at maximizing sensor networks lifetime. We propose three energy models for sensor network: a) a service-aware model that account for the specific role played by each node in a sensor network b) a sensor-aware model and c) load-balancing energy model that accounts for the sensor node fabric and its energy footprint. These two models are complemented by a load balancing model structured to balance energy consumption on the network of cluster heads that forms the backbone for any cluster-based hierarchical sensor network. We present two novel approaches for clustering the nodes of a hierarchical sensor network: a) a distanceaware clustering where nodes are clustered based on their distance and the residual energy and b) a service-aware clustering where the nodes of a sensor network are clustered according to their service offered to the network and their residual energy. These approaches are implemented into a family of routing protocols referred to as EOCIT (Energy Optimization using Clustering Techniques) which combines sensor node energy location and service awareness to achieve good network performance. Finally, building upon the Ant Colony Optimization System (ACS), Multipath Routing protocol based on Ant Colony Optimization approach for Wireless Sensor Networks (MRACO) is proposed as a novel multipath routing protocol that finds energy efficient routing paths for sensor readings dissemination from the cluster heads to the sink/base station of a hierarchical sensor network. Our simulation results reveal the relative efficiency of the newly proposed approaches compared to selected related routing protocols in terms of sensor network lifetime maximization

Applications of Prediction Approaches in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) collect data and continuously monitor ambient data such as temperature, humidity and light. The continuous data transmission of energy constrained sensor nodes is a challenge to the lifetime and performance of WSNs. The type of deployment environment is also and the network topology also contributes to the depletion of nodes which threatens the lifetime and the also the performance of the network. To overcome these challenges, a number of approaches have been proposed and implemented. Of these approaches are routing, clustering, prediction, and duty cycling. Prediction approaches may be used to schedule the sleep periods of nodes to improve the lifetime. The chapter discusses WSN deployment environment, energy conservation techniques, mobility in WSN, prediction approaches and their applications in scheduling the sleep/wake-up periods of sensor nodes

Optimizing communication and computation for multi-UAV information gathering applications

Typical mobile agent networks, such as multi-UAV systems, are constrained by limited resources: energy, computing power, memory and communication bandwidth. In particular, limited energy affects system performance directly, such as system lifetime. Moreover, it has been demonstrated experimentally in the wireless sensor network literature that the total energy consumption is often dominated by the communication cost, i.e. the computational and the sensing energy are small compared to the communication energy consumption. For this reason, the lifetime of the network can be extended significantly by minimizing the communication distance as well as the amount of communication data, at the expense of increasing computational cost. In this work, we aim at attaining an optimal trade-off between the communication and the computational energy. Specifically, we propose a mixed-integer optimization formulation for a multihop hierarchical clustering-based self-organizing UAV network incorporating data aggregation, to obtain an energy-efficient information routing scheme. The proposed framework is tested on two applications, namely target tracking and area mapping. Based on simulation results, our method can significantly save energy compared to a baseline strategy, where there is no data aggregation and clustering scheme

Performance and energy efficiency in wireless self-organized networks

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A Location-Aware Middleware Framework for Collaborative Visual Information Discovery and Retrieval

This work addresses the problem of scalable location-aware distributed indexing to enable the leveraging of collaborative effort for the construction and maintenance of world-scale visual maps and models which could support numerous activities including navigation, visual localization, persistent surveillance, structure from motion, and hazard or disaster detection. Current distributed approaches to mapping and modeling fail to incorporate global geospatial addressing and are limited in their functionality to customize search. Our solution is a peer-to-peer middleware framework based on XOR distance routing which employs a Hilbert Space curve addressing scheme in a novel distributed geographic index. This allows for a universal addressing scheme supporting publish and search in dynamic environments while ensuring global availability of the model and scalability with respect to geographic size and number of users. The framework is evaluated using large-scale network simulations and a search application that supports visual navigation in real-world experiments

Wireless sensor network as a distribute database

Wireless sensor networks (WSN) have played a role in various fields. In-network data processing is one of the most important and challenging techniques as it affects the key features of WSNs, which are energy consumption, nodes life circles and network performance. In the form of in-network processing, an intermediate node or aggregator will fuse or aggregate sensor data, which are collected from a group of sensors before transferring to the base station. The advantage of this approach is to minimize the amount of information transferred due to lack of computational resources. This thesis introduces the development of a hybrid in-network data processing for WSNs to fulfil the WSNs constraints. An architecture for in-network data processing were proposed in clustering level, data compression level and data mining level. The Neighbour-aware Multipath Cluster Aggregation (NMCA) is designed in the clustering level, which combines cluster-based and multipath approaches to process different packet loss rates. The data compression schemes and Optimal Dynamic Huffman (ODH) algorithm compressed data in the cluster head for the compressed level. A semantic data mining for fire detection was designed for extracting information from the raw data by the semantic data-mining model is developed to improve data accuracy and extract the fire event in the simulation. A demo in-door location system with in-network data processing approach is built to test the performance of the energy reduction of our designed strategy. In conclusion, the added benefits that the technical work can provide for in-network data processing is discussed and specific contributions and future work are highlighted

Optimal UAS Assignments and Trajectories for Persistent Surveillance and Data Collection from a Wireless Sensor Network

This research developed a method for multiple Unmanned Aircraft Systems (UAS) to efficiently collect data from a Wireless Sensor Networks (WSN). WSN are composed of any number of fixed, ground-based sensors that collect and upload local environmental data to over flying UAS. The three-step method first uniquely assigns aircraft to specific sensors on the ground. Second, an efficient flight path is calculated to minimize the aircraft flight time required to verify their assigned sensors. Finally, sensors reporting relatively higher rates of local environmental activity are re-assigned to dedicated aircraft tasked with concentrating on only those sensors. This work was sponsored by the Air Force Research Laboratory, Control Sciences branch, at Wright Patterson AFB. Based on simulated scenarios and preliminary flight tests, optimal flight paths resulted in a 14 to 32 reduction in flight time and distance when compared to traditional flight planning methods

Energy-efficient MAC protocol for wireless sensor networks

A Wireless Sensor Network (WSN) is a collection of tiny devices called sensor nodes which are deployed in an area to be monitored. Each node has one or more sensors with which they can measure the characteristics of their surroundings. In a typical WSN, the data gathered by each node is sent wirelessly through the network from one node to the next towards a central base station. Each node typically has a very limited energy supply. Therefore, in order for WSNs to have acceptable lifetimes, energy efficiency is a design goal that is of utmost importance and must be kept in mind at all levels of a WSN system. The main consumer of energy on a node is the wireless transceiver and therefore, the communications that occur between nodes should be carefully controlled so as not to waste energy. The Medium Access Control (MAC) protocol is directly in charge of managing the transceiver of a node. It determines when the transceiver is on/off and synchronizes the data exchanges among neighbouring nodes so as to prevent collisions etc., enabling useful communications to occur. The MAC protocol thus has a big impact on the overall energy efficiency of a node. Many WSN MAC protocols have been proposed in the literature but it was found that most were not optimized for the group of WSNs displaying very low volumes of traffic in the network. In low traffic WSNs, a major problem faced in the communications process is clock drift, which causes nodes to become unsynchronized. The MAC protocol must overcome this and other problems while expending as little energy as possible. Many useful WSN applications show low traffic characteristics and thus a new MAC protocol was developed which is aimed at this category of WSNs. The new protocol, Dynamic Preamble Sampling MAC (DPS-MAC) builds on the family of preamble sampling protocols which were found to be most suitable for low traffic WSNs. In contrast to the most energy efficient existing preamble sampling protocols, DPS-MAC does not cater for the worst case clock drift that can occur between two nodes. Rather, it dynamically learns the actual clock drift experienced between any two nodes and then adjusts its operation accordingly. By simulation it was shown that DPS-MAC requires less protocol overhead during the communication process and thus performs more energy efficiently than its predecessors under various network operating conditions. Furthermore, DPS-MAC is less prone to become overloaded or unstable in conditions of high traffic load and high contention levels respectively. These improvements cause the use of DPS-MAC to lead to longer node and network lifetimes, thus making low traffic WSNs more feasible.Dissertation (MEng)--University of Pretoria, 2008.Electrical, Electronic and Computer EngineeringMEngUnrestricte