Self-Orienting Wireless Multimedia Sensor Networks for Maximizing Multimedia Coverage

Abstract—The performance of a wireless multimedia sensor network (WMSN) is tightly coupled with the pose of individual multimedia sensors. In particular, orientation of an individual multimedia sensor (direction of its sensing unit) is of great importance for the sensor network applications in order to capture the entire image of the field. In this paper, we study the problem of self-orientation in a wireless multimedia sensor network, that is finding the most beneficial pose of multimedia sensors to maximize multimedia coverage with occlusion-free viewpoints. We first propose a distributed algorithm to detect a node’s multimedia coverage and then determine its orientation, while minimizing the effect of occlusions and total overlapping regions in the sensing field. Our approach enables multimedia sensor nodes to compute their directional coverage, provisioning self-configurable sensor orientations in an efficient way. Simulations show that using distributed messaging and self-orientation having occlusion-free viewpoints significantly increase the multimedia coverage. I

Controlling the mobility and enhancing the performance of multiple message ferries in delay tolerant networks

In einem drahtlosen Netzwerk mit isolierten und stationären Knoten können Adhoc und verzögerungstolerante Netzwerk Routing-Protokolle nicht verwendet werden. Message Ferry Netzwerke sind die Lösung für diese Fälle, in denen ein (oder mehrere) Message Ferry Knoten den store-carry-forward Mechanismus verwendet und zwischen den Knoten reist, um Nachrichten auszutauschen. In diesem Fall erfahren die Nachrichten für gewöhnlich eine lange Verzögerung. Um die Performance der Message Ferry Netzwerke zu verbessern, kann die Mobilität der Message Ferry Knoten gesteuert werden. In dieser Doktorarbeit werden zwei Strategien zur Steuerung der Mobilität der Message Ferry Knoten studiert. Die Strategien sind das on-the-fly Entscheidungsverfahren in Ferry Knoten und die offline Wegplanung für Ferry Knoten. Für die on-the-fly Strategie untersucht diese Arbeit Decision-maker in Ferry Knoten, der die Entscheidung auf Grundlage der lokalen Observation eines Ferry Knoten trifft. Zur Koordinierung mehrerer Ferry Knoten, die keine globale Kenntnis über das Netzwerk haben, wird eine indirekte Signalisierung zwischen Ferry Knoten vorgeschlagen. Zur Kooperation der Ferry Knoten für die Zustellung der Nachrichten werden einige Ansätze zum Nachrichtenaustausch zwischen Ferry Knoten vorgeschlagen, in denen der Decision-maker eines Ferry Knotens seine Information mit dem verzögerungstoleranten Router des Ferry Knoten teilt, um die Effizienz des Nachrichtenaustauschs zwischen Ferry Knoten zu verbessern. Umfangreiche Simulationsstudien werden zur Untersuchung der vorgeschlagenen Ansätze und des Einflusses verschiedener Nachrichtenverkehrsszenarien vorgenommen. Außerdem werden verschiedene Szenarien mit unterschiedlicher Anzahl von Ferry Knoten, verschiedener Geschwindigkeit der Ferry Knoten und verschiedener Ansätze zum Nachrichtenaustausch zwischen Ferry Knoten studiert. Zur Evaluierung der offline Wegplanungsstrategie wird das Problem als Multiple Traveling Salesmen Problem (mTSP) modelliert und ein genetischer Algorithmus zur Approximation der Lösung verwendet. Es werden verschiedene Netzwerkarchitekturen zur Pfadplanung der Ferry Knoten vorgestellt und studiert. Schließlich werden die Strategien zur Steuerung der Mobilität der Ferry Knoten verglichen. Die Ergebnisse zeigen, dass die Performance der Strategien in Bezug auf die Ende-zu-Ende-Verzögerung von dem Szenario des Nachrichtenverkehrs abhängt. In Szenarien, wie Nachrichtenverkehr in Sensor-Netzwerken, in denen ein Knoten die Nachrichten zu allen anderen Knoten sendet oder von allen anderen Knoten empfängt, zeigt die offline Wegplanung, basierend auf der mTSP Lösung, bessere Performance als die on-the-fly Strategie. Andererseits ist die on-the-fly Stratgie eine bessere Wahl in Szenarien wie Nachrichtenaustausch zwischen Rettungskräften während einer Katastrophe, in denen alle drahtlose Knoten die Nachrichten austauschen müssen. Zudem ist die on-the-fly Strategie flexibler, robuster als offline Wegplanung und benötigt keine Initialisierungszeit.In a wireless network with isolated and stationary nodes, ad hoc and delay tolerant routing approaches fail to deliver messages. Message ferry networks are the solution for such networks where one or multiple mobile nodes, i.e. message ferry, apply the store-carry-forward mechanism and travel between nodes to exchange their messages. Messages usually experience a long delivery delay in this type of network. To improve the performance of message ferry networks, the mobility of ferries can be controlled. In this thesis, two main strategies to control mobility of multiple message ferries are studied. The strategies are the on-the-fly mobility decision making in ferries and the offline path planning for ferries. To apply the on-the-fly strategy, this work proposes a decision maker in ferries which makes mobility decisions based on the local observations of ferries. To coordinate multiple ferries, which have no global view from the network, an indirect signaling of ferries is proposed. For cooperation of ferries in message delivery, message forwarding and replication schemes are proposed where the mobility decision maker shares its information with the delay tolerant router of ferries to improve the efficiency of message exchange between ferries. An extensive simulation study is performed to investigate the performance of the proposed schemes and the impact of different traffic scenarios in a network. Moreover, different scenarios with different number of ferries, different speed of ferries and different message exchange approaches between ferries are studied. To study the offline path planning strategy, the problem is modeled as multiple traveling salesmen problem (mTSP) and a genetic algorithm is applied to approximate the solution. Different network architectures are proposed and studied where the path of ferries are planned in advance. Finally, the strategies to control the mobility of ferries are compared. The results show that the performance of each strategy, in terms of the average end-to-end delay of messages, depends on the traffic scenario in a network. In traffic scenarios same as the traffic in sensor networks, where only a single node generates messages to all nodes or receives messages from all node, the offline path planning based on mTSP solution performs better than the on-the-fly decision making. On the other hand, in traffic scenarios same as the traffic in disaster scenarios, where all nodes in a network may send and receive messages, the on-the-fly decision making provides a better performance. Moreover, the on-thy-fly decision making is always more flexible, more robust and does not need any initialization time

Multi-UAV Data Collection Framework for Wireless Sensor Networks

In this paper, we propose a framework design for wireless sensor networks based on multiple unmanned aerial vehicles (UAVs). Specifically, we aim to minimize deployment and operational costs, with respect to budget and power constraints. To this end, we first optimize the number and locations of cluster heads (CHs) guaranteeing data collection from all sensors. Then, to minimize the data collection flight time, we optimize the number and trajectories of UAVs. Accordingly, we distinguish two trajectory approaches: 1) where a UAV hovers exactly above the visited CH; and 2) where a UAV hovers within a range of the CH. The results of this include guidelines for data collection design. The characteristics of sensor nodes' K-means clustering are then discussed. Next, we illustrate the performance of optimal and heuristic solutions for trajectory planning. The genetic algorithm is shown to be near-optimal with only $3.5\%$ degradation. The impacts of the trajectory approach, environment, and UAVs' altitude are investigated. Finally, fairness of UAVs trajectories is discussed.Comment: To be presented at 2019 IEEE Global Communications Conference (Globecom

Efficient approach for maximizing lifespan in wireless sensor networks by using mobile sinks

Recently, sink mobility has been shown to be highly beneficial in improving network lifetime in wireless sensor networks (WSNs). Numerous studies have exploited mobile sinks (MSs) to collect sensed data in order to improve energy efficiency and reduce WSN operational costs. However, there have been few studies on the effectiveness of MS operation on WSN closed operating cycles. Therefore, it is important to investigate how data is collected and how to plan the trajectory of the MS in order to gather data in time, reduce energy consumption, and improve WSN network lifetime. In this study, we combine two methods, the cluster-head election algorithm and the MS trajectory optimization algorithm, to propose the optimal MS movement strategy. This study aims to provide a closed operating cycle for WSNs, by which the energy consumption and running time of a WSN is minimized during the cluster election and data gathering periods. Furthermore, our flexible MS movement scenarios achieve both a long network lifetime and an optimal MS schedule. The simulation results demonstrate that our proposed algorithm achieves better performance than other well-known algorithms

A Cloud Based Disaster Management System

The combination of wireless sensor networks (WSNs) and 3D virtual environments opens a new paradigm for their use in natural disaster management applications. It is important to have a realistic virtual environment based on datasets received from WSNs to prepare a backup rescue scenario with an acceptable response time. This paper describes a complete cloud-based system that collects data from wireless sensor nodes deployed in real environments and then builds a 3D environment in near real-time to reflect the incident detected by sensors (fire, gas leaking, etc.). The system’s purpose is to be used as a training environment for a rescue team to develop various rescue plans before they are applied in real emergency situations. The proposed cloud architecture combines 3D data streaming and sensor data collection to build an efficient network infrastructure that meets the strict network latency requirements for 3D mobile disaster applications. As compared to other existing systems, the proposed system is truly complete. First, it collects data from sensor nodes and then transfers it using an enhanced Routing Protocol for Low-Power and Lossy Networks (RLP). A 3D modular visualizer with a dynamic game engine was also developed in the cloud for near-real time 3D rendering. This is an advantage for highly-complex rendering algorithms and less powerful devices. An Extensible Markup Language (XML) atomic action concept was used to inject 3D scene modifications into the game engine without stopping or restarting the engine. Finally, a multi-objective multiple traveling salesman problem (AHP-MTSP) algorithm is proposed to generate an efficient rescue plan by assigning robots and multiple unmanned aerial vehicles to disaster target locations, while minimizing a set of predefined objectives that depend on the situation. The results demonstrate that immediate feedback obtained from the reconstructed 3D environment can help to investigate what–if scenarios, allowing for the preparation of effective rescue plans with an appropriate management effort.info:eu-repo/semantics/publishedVersio

Parallel genetic approach for routing optimization in large ad hoc networks

This article presents a new approach of integrating parallelism into the genetic algorithm (GA), to solve the problem of routing in a large ad hoc network, the goal is to find the shortest path routing. Firstly, we fix the source and destination, and we use the variable-length chromosomes (routes) and their genes (nodes), in our work we have answered the following question: what is the better solution to find the shortest path: the sequential or parallel method?. All modern systems support simultaneous processes and threads, processes are instances of programs that generally run independently, for example, if you start a program, the operating system spawns a new process that runs parallel elements to other programs, within these processes, we can use threads to execute code simultaneously. Therefore, we can make the most of the available central processing unit (CPU) cores. Furthermore, the obtained results showed that our algorithm gives a much better quality of solutions. Thereafter, we propose an example of a network with 40 nodes, to study the difference between the sequential and parallel methods, then we increased the number of sensors to 100 nodes, to solve the problem of the shortest path in a large ad hoc network

An energy-balanced heuristic for mobile sink scheduling in hybrid WSNs

Wireless sensor networks (WSNs) are integrated as a pillar of collaborative Internet of Things (IoT) technologies for the creation of pervasive smart environments. Generally, IoT end nodes (or WSN sensors) can be mobile or static. In this kind of hybrid WSNs, mobile sinks move to predetermined sink locations to gather data sensed by static sensors. Scheduling mobile sinks energyefficiently while prolonging the network lifetime is a challenge. To remedy this issue, we propose a three-phase energy-balanced heuristic. Specifically, the network region is first divided into grid cells with the same geo-graphical size. These grid cells are assigned to clusters through an algorithm inspired by the k-dimensional tree algorithm, such that the energy consumption of each clus-ter is similar when gathering data. These clusters are adjusted by (de)allocating grid cells contained in these clusters, while considering the energy consumption of sink movement. Consequently, the energy to be consumed in each cluster is approximately balanced considering the energy consumption of both data gathering and sink movement. Experimental evaluation shows that this technique can generate an optimal grid cell division within a limited time of iterations and prolong the network lifetime.This work was supported in part by the National Natural Science Foundation of China under Grant 61379126, Grant 61401107, Grant 61572060, and Grant 61170296; in part by the Scientific Research Foundation for Returned Scholars, Ministry of Education of China; and in part by the Fundamental Research Funds for the Central Universities. Paper no. TII-15-0703.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424hb2017Electrical, Electronic and Computer Engineerin

Energy efficient wireless sensor network topologies and routing for structural health monitoring.

The applicability of wireless sensor networks (WSNs) has dramatically increased from the era of smart farming and environmental monitoring to the recent commercially successful internet of things (IoT) applications. Simultaneously, diversity in WSN applications has led to the application of specific performance requirements, such as fault tolerance, reliability, robustness and survivability. One important application is structural health monitoring (SHM) in airplanes. Airborne Wireless Sensor Network (AWSN) have received considerable attention in recent times, owing to the many issues that are intrinsic to traditional wire-based airplane monitoring systems, such as complicated cable routing, long wiring, wiring degradation over time, installation overhead, etc. This project examines the SHM of aircraft wing and WSN design (ZigBee), and aspects such as node deployment and power efficient routing, vis-à-vis energy harvesting. Node deployment and power efficient routing protocol are related problems, and so this thesis proposes solutions using optimization techniques for Ant Colony Optimization (ACO), and power transmission profiling using Computer Simulation Technology software (CST). There are three wing models; namely NACA64A410 model, Empty NACA64A410 model for the Wing, and Empty Prismatic model of the wing was specified and simulated in CST software. A simulation was carried out between the frequencies of 100 MHz to 5 GHz, and identified significant variations in the Sij parameter between the frequency range 2.4GHz and 2.5GHz. Critical analysis of the obtained results revealed the presence of a significant impact from wing shape and the wing’s inner structure on possible radio wave propagation in the aircraft wing. The different material composition of aircraft wings was also examined to establish the influence of aircraft wing material on radio wave propagation in an aircraft wing. The three materials tested were Perfect Electrical Conductor (PEC), Aluminium, and Carbon Fibre Composites (CFCs). For power transmission profiling (Sij parameter), 130 nodes were deployed in regular and periodic compartments, created by ribs and spars, usually at vantage points and rib openings, so that a direct line of sight could be established. However, four sink nodes were also placed at the wing root, as presented in NC37 and NC38 simulations for aluminium and CFC wing models respectively. The evaluation of signal propagation in aluminium and CFC aircraft wing models revealed CFC wing models allow less transmission than aluminium wing models. A multiple Travelling Salesman (mTSP) problem was formulated and solved, using Ant Colony Optimization in MATLAB to identify optimal topology and optimal routes to support radio propagation in ZigBee networks. Then solving the mTSP problem for different regular deployments of nodes in the wing geometry, it was found that an edgewise communication route was the shortest route for a large number of nodes, wherein 4 fixed sink nodes were placed at the wing root. For a realistic wing model, the different possible configuration of ZigBee units were deduced using rational reasoning, based on results from empty wing models. Besides the determined S-parameter, aircraft wing materials and optimal nodes, the residual energy of each sensor node is also considered an essential criterion to improve the efficiency of ZigBee communications on the aircraft wing. Therefore, a novel hybrid protocol called the Energy-Opportunistic Weighted Minimum Energy (EOWEME) protocol can be formulated and implemented in MATLAB. The comparative results revealed the energy saving of EOWEME protocol is 20% higher compared to the Ad Hoc On-Demand Distance Vector (AODV) routing protocol. However, the need for further energy savings resulted in development of an improved EOWEME protocol when incorporating the clustering concept and the previously determined S-parameter, a number of nodes, and their radiation patterns. Critical evaluation of this improved EOWEME protocol showed a maximum of 10% higher energy savings than the previous EOWEME protocol. To summarize key insights and the results of this thesis, it is apparent that the thesis addresses SHM in aircraft wings, using WSNs from a holistic perspective with the following major contributions, • CST simulations identify power transfer (S-parameter) profiling in various wing models, with no internal structural elements to identify realistic wing with spars, and bars. With an average S-parameter of -107 dB at around 3 m, the communication or transmission range of 1 m was identified to minimize loss of transmitted power. A range less than 1m would cause issues such as interference, reflection etc. • Using a transmission range of 1 m, WSN nodes were assessed for shortest route commensurate with energy efficient packet transmission to sink node from the farthest node; i.e. near the wing tip. The shortest routes converged to travel along the length of the wing in the case of an empty wing model, however it was also observed in a realistic wing model, where internal structural elements constrained node deployment. An average distance of nearly 13 m required data transmitted from the farthest nodes to reach the sink nodes. Increasing the nodes however increased the distance required to up to 20 m in the case of 240 nodes. • A new routing protocol, EOWEME was formulated, showing 20% greater energy savings than AODV in the realistic wing model.PhD in Aerospac

Dynamic Multi-Objective Auction-Based (DYMO-Auction) Task Allocation

In this paper, we address the problem of online dynamic multi-robot task allocation (MRTA) problem. In the existing literature, several works investigated this problem as a multi-objective optimization (MOO) problem and proposed different approaches to solve it including heuristic methods. Existing works attempted to find Pareto-optimal solutions to the MOO problem. However, to the best of authors’ knowledge, none of the existing works used the task quality as an objective to optimize. In this paper, we address this gap, and we propose a new method, distributed multi-objective task allocation approach (DYMO-Auction), that considers tasks’ quality requirement, along with travel distance and load balancing. A robot is capable of performing the same task with different levels of perfection, and a task needs to be performed with a level of perfection. We call this level of perfection quality level. We designed a new utility function to consider four competing metrics, namely the cost, energy, distance, type of tasks. It assigns the tasks dynamically as they emerge without global information and selects the auctioneer randomly for each new task to avoid the single point of failure. Extensive simulation experiments using a 3D Webots simulator are conducted to evaluate the performance of the proposed DYMO-Auction. DYMO-Auction is compared with the sequential single-item approach (SSI), which requires global information and offline calculations, and with Fuzzy Logic Multiple Traveling Salesman Problem (FL-MTSP) approach. The results demonstrate a proper matching with SSI in terms of quality satisfaction and load balancing. However, DYMO-Auction demands 20% more travel distance. We experimented with DYMO-Auction using real Turtlebot2 robots. The results of simulation experiments and prototype experiments follow the same trend. This demonstrates the usefulness and practicality of the proposed method in real-world scenarios.This research was funded by the National Plan for Science, Technology, and Innovation (MAARIFAH)—King Abdulaziz City for Science and Technology through the Science and Technology Unit at King Fahd University of Petroleum and Minerals (KFUPM), the Kingdom of Saudi Arabia, award project No. 11-ELE2147-4. In addition, Anis Koubaa would like to acknowledge the support by the Robotics and Internet-of-Things Lab of Prince Sultan Universityinfo:eu-repo/semantics/publishedVersio