Adaptive epidemic dissemination as a finite-horizon optimal stopping problem

Wireless ad hoc networks are characterized by their limited capabilities and their routine deployment in unfavorable environments. This creates the strong requirement to regulate energy expenditure. We present a scheme to regulate energy cost through optimized transmission scheduling in a noisy epidemic dissemination environment. Building on the intrinsically cross-layer nature of the adaptive epidemic dissemination process, we strive to deliver an optimized mechanism, where energy cost is regulated without compromising the network infection. Improvement of data freshness and applicability in routing are also investigated. Extensive simulations are used to support our proposal

An efficient scheme for applying software updates in pervasive computing applications

The Internet of Things (IoT) offers a vast infrastructure of numerous interconnected devices capable of communicating and exchanging data. Pervasive computing applications can be formulated on top of the IoT involving nodes that can interact with their environment and perform various processing tasks. Any task is part of intelligent services executed in nodes or the back end infrastructure for supporting end users’ applications. In this setting, one can identify the need for applying updates in the software/firmware of the autonomous nodes. Updates are extensions or patches significant for the efficient functioning of nodes. Legacy methodologies deal with centralized approaches where complex protocols are adopted to support the distribution of the updates in the entire network. In this paper, we depart from the relevant literature and propose a distributed model where each node is responsible to, independently, initiate and conclude the update process. Nodes monitor a set of metrics related to their load and the performance of the network and through a time-optimized scheme identify the appropriate time to conclude the update process. We report on an infinite horizon optimal stopping model on top of the collected performance data. The aim is to make nodes capable of identifying when their performance and the performance of the network are of high quality to efficiently conclude the update process. We provide specific formulations and the analysis of the problem while extensive simulations and a comparison assessment reveal the advantages of the proposed solution

Time-Optimized Contextual Information Flow on Unmanned Vehicles

Nowadays, the domain of robotics experiences a significant growth. We focus on Unmanned Vehicles intended for the air, sea and ground (UxV). Such devices are typically equipped with numerous sensors that detect contextual parameters from the broader environment, e.g., obstacles, temperature. Sensors report their findings (telemetry) to other systems, e.g., back-end systems, that further process the captured information while the UxV receives control inputs, such as navigation commands from other systems, e.g., commanding stations. We investigate a framework that monitors network condition parameters including signal strength and prioritizes the transmission of control messages and telemetry. This framework relies on the Theory of Optimal Stopping to assess in real-time the trade-off between the delivery of the messages and the network quality statistics and optimally schedules critical information delivery to back-end systems

Efficient Broadcast in Opportunistic Networks using Optimal Stopping Theory

In this paper, we present a broadcast dissemination protocol for messages in opportunistic networks (OppNet) that is efficient in terms of energy consumption and network capacity usage, while not increasing the number of excluded nodes (nodes not receiving messages). The majority of the OppNet broadcast delivery schemes proposed in the literature, do not take into consideration that reducing energy and buffer usage is of paramount importance in these wireless networks normally consisting of small devices. In our protocol, broadcast messages are limited by carefully selecting their prospective forwarders (storers). The keystone of our protocol is the use of Optimal Stopping Theory, which selects the best message storers at every stage of the algorithm, while holding back broad message dissemination until convenient conditions are met. The broadcast efficiency of the proposed protocol out competes other OppNet broadcast proposals in four well-known scenarios. Furthermore, the protocol reduces the number of both dropped messages and nodes not receiving messages, thus maximising network capacity usage, and the span of the message deliver

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

To transmit or not to transmit: controlling the communications in the mobile IoT domain

The Mobile IoT domain has been significantly expanded with the proliferation of drones and unmanned robotic devices. In this new landscape, the communication between the resource-constrained device and the fixed infrastructure is similarly expanded to include new messages of varying importance, control, and monitoring. To efficiently and effectively control the exchange of such messages subject to the stochastic nature of the underlying wireless network, we design a time-optimized, dynamic, and distributed decision-making mechanism based on the principles of the Optimal Stopping and Change Detection theories. The findings from our experimentation platform are promising and solidly supportive to a vast spectrum of real-time and latency-sensitive applications with quality-of-service requirements in mobile IoT environments

Optimal Control of Epidemics in the Presence of Heterogeneity

We seek to identify and address how different types of heterogeneity affect the optimal control of epidemic processes in social, biological, and computer networks. Epidemic processes encompass a variety of models of propagation that are based on contact between agents. Assumptions of homogeneity of communication rates, resources, and epidemics themselves in prior literature gloss over the heterogeneities inherent to such networks and lead to the design of sub-optimal control policies. However, the added complexity that comes with a more nuanced view of such networks complicates the generalizing of most prior work and necessitates the use of new analytical methods. We first create a taxonomy of heterogeneity in the spread of epidemics. We then model the evolution of heterogeneous epidemics in the realms of biology and sociology, as well as those arising from practice in the fields of communication networks (e.g., DTN message routing) and security (e.g., malware spread and patching). In each case, we obtain computational frameworks using Pontryagin’s Maximum Principle that will lead to the derivation of dynamic controls that optimize general, context-specific objectives. We then prove structures for each of these vectors of optimal controls that can simplify the derivation, storage, and implementation of optimal policies. Finally, using simulations and real-world traces, we examine the benefits achieved by including heterogeneity in the control decision, as well as the sensitivity of the models and the controls to model parameters in each case

Dtn and non-dtn routing protocols for inter-cubesat communications: A comprehensive survey

CubeSats, which are limited by size and mass, have limited functionality. These miniaturised satellites suffer from a low power budget, short radio range, low transmission speeds, and limited data storage capacity. Regardless of these limitations, CubeSats have been deployed to carry out many research missions, such as gravity mapping and the tracking of forest fires. One method of increasing their functionality and reducing their limitations is to form CubeSat networks, or swarms, where many CubeSats work together to carry out a mission. Nevertheless, the network might have intermittent connectivity and, accordingly, data communication becomes challenging in such a disjointed network where there is no contemporaneous path between source and destination due to satellites’ mobility pattern and given the limitations of range. In this survey, various inter-satellite routing protocols that are Delay Tolerant (DTN) and Non Delay Tolerant (Non-DTN) are considered. DTN routing protocols are considered for the scenarios where the network is disjointed with no contemporaneous path between a source and a destination. We qualitatively compare all of the above routing protocols to highlight the positive and negative points under different network constraints. We conclude that the performance of routing protocols used in aerospace communications is highly dependent on the evolving topology of the network over time. Additionally, the Non-DTN routing protocols will work efficiently if the network is dense enough to establish reliable links between CubeSats. Emphasis is also given to network capacity in terms of how buffer, energy, bandwidth, and contact duration influence the performance of DTN routing protocols, where, for example, flooding-based DTN protocols can provide superior performance in terms of maximizing delivery ratio and minimizing a delivery delay. However, such protocols are not suitable for CubeSat networks, as they harvest the limited resources of these tiny satellites and they are contrasted with forwarding-based DTN routing protocols, which are resource-friendly and produce minimum overheads on the cost of degraded delivery probability. From the literature, we found that quota-based DTN routing protocols can provide the necessary balance between delivery delay and overhead costs in many CubeSat missions

Adaptive UxV Routing Based on Network Performance

Μια μεγάλη και απότομη εξέλιξη παρατηρείται σήμερα στον τομέα της ρομποτικής και του διαδικτύου των πραγμάτων. Οι κόμβοι που αποτελούν την κύρια υποδομή του διαδικτύου των πραγμάτων έχουν εμπλουτιστεί με σημαντικές και πολυποίκιλες δυνατότητες. Η πιο σημαντική από αυτες τις δυνατότηες είναι η κινητικότητα, η οποία έχει προσφερθεί λόγω της επίσης σημαντικής εξέλιξης του τομέα που αφορά τα μη επανδρωμένα οχήματα. Ένα μη επανδρωμένο όχημα μπορεί να εξυπηρετήσει έναν ερευνητή ως κινητός αισθητήρας (θερμοκρασίας, πίεσης νερού) και να τοποθετηθεί σε οποιαδήποτε δυνατή τοποθεσία. Κάποια ακόμα χαρακτηριστικά που κάνουν δελεαστική την επιλογή μη επανδρωμένων οχημάτων ως κόμβους του διαδικτύου των πραγμάτων είναι η ικανότητα της λήψης αποφάσεων χωρίς την ανθρώπινη παρέμβαση, η αντοχή, η επαναπρογραμματισιμότητα καθώς και η δυνατότητα της ζωντανής ροής πολυμέσων. Με βάση αυτά τα χαρακτηριστικά τα μη επανδρωμένα οχήματα μπορούν να χρησιμοποιηθούν επίσης σε περιπτώσεις εποπτείας χώρων και συνόρων, παρακολούθηση καμερών ασφαλείας καθώς και για υποστήριξη σε περιπτώσεις διαχείρισης κρίσεων. Για παράδειγμα ένα μη επανδρωμένο όχημα ξηράς όπου φέρει μία υψηλής ευκρίνειας κάμερα, σε συνδυασμό με έναν αλγόριθμο αναγνώρισης αντικειμένων μπορεί να χρησιμοποιηθεί για επόπτεια συνόρων. Σε αυτήν την διπλωματική εργασία προτείνεται ένα πλαίσιο, στο οποίο υλοποιείται μια διαδικασία λήψης αποφάσεων με βάση την ποιότητα του δικτύου. Το πλαίσιο αυτό προσαρμόζει την ροή της πληροφορίας μεταξύ του επανδρωμένου οχήματος και του σταθμού ελέγχου, βασισμένο σε μετρικές ποιότητας του δικτύου (όπως το ρυθμό απώλειας πακέτων) και στις αρχές της Θεωρίας Βέλτιστης Παύσης, με σκοπό να εξασφαλίσει το βέλτιστο ποσοστό παραλαβής πληροφοριών υψίστης σημασίας από το μη επανδρωμένο όχημα προς το σταθμό ελέγχου και το αντίστροφο. Όταν το δίκτυο συμπεριφέρεται άριστα δεν υπάρχει περιορισμός στην ροή πληροφοριών, αλλα έαν το δίκτυο είναι είτε υπερφορτωμένο, είτε κορεσμένο, τότε εφαρμόζονται περιοριστικοί κανόνες. Το προτεινόμενο μοντέλο, εισάγει δύο μηχανισμούς βέλτιστης παύσης βασισμένος στην Θεωρίας Βέλτιστης Παύσης, στη Θεωρία Ανίχνευσης Αλλαγής Κατεύθυνσης καθώς και σε μία διαδικασία εκπτωτικής ανταμοιβής. Για την υποστήριξη του υλοποιημένου πλαισίου, έγινε μία σειρά πειραμάτων με πολύ υποσχόμενα αποτελέσματα. Σαν κινητός κόμβος χρησιμοποιήθηκε ένα ρομπότ TurtleBot, μαζί με ένα XBOX Kinect που έφερε μία έγχρωμη κάμερα και έναν αισθητήρα βάθους καθώς και με ένα Raspberry Pi, το οποίο εκτελούσε το Robotic Operating System (ROS) και το σύστημα Apache Kafka, με σκοπό να γεφυρώσει το χάσμα επικοινωνίας μεταξύ TurtleBot και σταθμού ελέγχου.Robotics and Internet of Things (IoT) have been experiencing rapid growth nowadays. IoT nodes are significantly enhanced with many different features. One of the most important is the mobility capabilities, given by the noticeably huge growth of UxV (UxVs- x stands for a different type of environment, i.e. ‘s’ stands for sea, ‘a’ for air and ‘g’ for ground) area. The idea is the assumption of a drone as a mobile sensor, that can be deployed wherever the experimenter wants. Some more characteristics that make the unmanned vehicles a very tempting decision as IoT nodes are the decision-making ability without human interaction, endurance, re-programmability and capability of multimedia streaming. These characteristics make drones an option for use cases of surveillance, security monitoring, and supporting crisis management activities. For instance, a UGV equipped with a high-definition camera and running an algorithm of object recognition can serve the purpose of border surveillance. In this thesis, a framework that implements a network quality based decision-making process is developed. This framework adapts the information flow between the UxV and the Ground Control Station (GCS) based on network quality metrics (such as packet error rate etc.) and the principals Optimal Stopping Theory (OST). The goal of this framework is to ensure the optimal delivery of critical information from UxV to GCS and vice-versa. If the network behaves optimally then there is no limitation on the information flow, but if the network is saturated or overloaded restriction rules are applied. The proposed model introduces two optimal stopping time mechanisms based on change detection theory and a discounted reward process. To support the implemented framework, an experimental environment has been set up and also a series of experiments with very promising results. As a mobile IoT node, a TurtleBot has been used, along with an XBOX Kinect sensor (RGB camera and depth sensor) and a Raspberry Pi running Robotic Operating System (ROS) and Apache Kafka pub-sub system with ultimate purpose the communication between the TurtleBot and the GCS