Actor coordination using info-gap decision theory in wireless sensor and actor networks

Mobile, unmanned, power and resource-rich devices, called actors, deployed within a Wireless Sensor Network (WSN) application area, enable faster response times to events. Due to cost constraints, only a few actors can be placed within a WSN application area. Determining which actor or set of actors should respond to an event is important, because the correct decision will increase the event response time, and reduce energy expenditure. Since the mobile actors are widely dispersed over the application area, the actors' accurate location and energy details will not always be available. In this paper, we show that using info-gap decision theory to choose the correct actors to respond to an event when uncertainty about an actor's location and/or energy exists, ensures that the actors chosen can adequately respond to the event. The robustness of the decision choice of the set of actor(s) assigned to respond to an event means that all chosen actor(s) have sufficient energy to respond to the event in real-time.http://www.inderscience.com/browse/index.php?journalCODE=ijsnetai201

Energy efficient communication models in wireless sensor and actor networks

Sensor nodes in a wireless sensor network (WSN) have a small, non-rechargeable power supply. Each message transmission or reception depletes a sensor node’s energy. Many WSN applications are ad-hoc deployments where a sensor node is only aware of its immediate neighbours. The lack of a predefined route path and the need to restrict the amount of communication that occurs within the application area impose constraints on WSNs not prevalent in other types of networks. An area of active research has been how to notify the central sink (or monitoring hub) about an event in real-time by utilising the minimum number of messages to route a message from a source node to the destination sink node. In this thesis, strategies to limit communication within a WSN application area, while ensuring that events are reported on and responded to in real-time, is presented. A solution based on modelling a WSN as a small world network and then transmitting an initialisation message (IM) on network start-up to create multiple route paths from any sensor node to one or more sinks is proposed. The reason for modelling a WSN as a small world network is to reduce the number of nodes required to re-transmit a message from a source sensor node to a sink. The purpose of sending an IM at network start-up is to ensure that communication within the WSN is minimised. When routing a message to a static sink, the nodes closest to the static sink receive a disproportionate number of messages, resulting in their energy being consumed earlier. The use of mobile sinks has been proposed but to our knowledge no studies have been undertaken on the paths these mobile sinks should follow. An algorithm to determine the optimum path for mobile sinks to follow in a WSN application area is described. The purpose of an optimum path is to allow more equitable usage of all nodes to transfer an event message to a mobile sink. The idea of using multiple static sinks placed at specific points in the small world model is broadened to include using multiple mobile sinks called actors to move within a WSN application area and respond to an event in real-time. Current coordination solutions to determine which actor(s) must respond to the event result in excessive message communication and limit the real-time response to an event. An info gap decision theory (IGDT) model to coordinate which actor or set of actors should respond to the event is described. A comparison of the small world routing (SWR) model against routing using flooding and gossiping shows that the SWR model significantly reduces the number of messages transmitted within the network. An analysis of the number of IMs transmitted and received at individual node level shows that prudent selection of the hop count (number of additional nodes required to route a message to sink) to a sink node will result in a reduced number of messages transmitted and received per node within the network. The use of the IGDT model results in a robust decision on the actor(s) chosen to respond to an event even when uncertainty about the location and available energy of other actor(s) exists.Thesis (PhD(Eng))--University of Pretoria, 2012.Electrical, Electronic and Computer Engineeringunrestricte

Intelligent and Secure Underwater Acoustic Communication Networks

Underwater acoustic (UWA) communication networks are promising techniques for medium- to long-range wireless information transfer in aquatic applications. The harsh and dynamic water environment poses grand challenges to the design of UWA networks. This dissertation leverages the advances in machine learning and signal processing to develop intelligent and secure UWA communication networks. Three research topics are studied: 1) reinforcement learning (RL)-based adaptive transmission in UWA channels; 2) reinforcement learning-based adaptive trajectory planning for autonomous underwater vehicles (AUVs) in under-ice environments; 3) signal alignment to secure underwater coordinated multipoint (CoMP) transmissions. First, a RL-based algorithm is developed for adaptive transmission in long-term operating UWA point-to-point communication systems. The UWA channel dynamics are learned and exploited to trade off energy consumption with information delivery latency. The adaptive transmission problem is formulated as a partially observable Markov decision process (POMDP) which is solved by a Monte Carlo sampling-based approach, and an expectation-maximization-type of algorithm is developed to recursively estimate the channel model parameters. The experimental data processing reveals that the proposed algorithm achieves a good balance between energy efficiency and information delivery latency. Secondly, an online learning-based algorithm is developed for adaptive trajectory planning of multiple AUVs in under-ice environments to reconstruct a water parameter field of interest. The field knowledge is learned online to guide the trajectories of AUVs for collection of informative water parameter samples in the near future. The trajectory planning problem is formulated as a Markov decision process (MDP) which is solved by an actor-critic algorithm, where the field knowledge is estimated online using the Gaussian process regression. The simulation results show that the proposed algorithm achieves the performance close to a benchmark method that assumes perfect field knowledge. Thirdly, the dissertation presents a signal alignment method to secure underwater CoMP transmissions of geographically distributed antenna elements (DAEs) against eavesdropping. Exploiting the low sound speed in water and the spatial diversity of DAEs, the signal alignment method is developed such that useful signals will collide at the eavesdropper while stay collision-free at the legitimate user. The signal alignment mechanism is formulated as a mixed integer and nonlinear optimization problem which is solved through a combination of the simulated annealing method and the linear programming. Taking the orthogonal frequency-division multiplexing (OFDM) as the modulation technique, simulation and emulated experimental results demonstrate that the proposed method significantly degrades the eavesdropper\u27s interception capability

Contributions to Edge Computing

Efforts related to Internet of Things (IoT), Cyber-Physical Systems (CPS), Machine to Machine (M2M) technologies, Industrial Internet, and Smart Cities aim to improve society through the coordination of distributed devices and analysis of resulting data. By the year 2020 there will be an estimated 50 billion network connected devices globally and 43 trillion gigabytes of electronic data. Current practices of moving data directly from end-devices to remote and potentially distant cloud computing services will not be sufficient to manage future device and data growth. Edge Computing is the migration of computational functionality to sources of data generation. The importance of edge computing increases with the size and complexity of devices and resulting data. In addition, the coordination of global edge-to-edge communications, shared resources, high-level application scheduling, monitoring, measurement, and Quality of Service (QoS) enforcement will be critical to address the rapid growth of connected devices and associated data. We present a new distributed agent-based framework designed to address the challenges of edge computing. This actor-model framework implementation is designed to manage large numbers of geographically distributed services, comprised from heterogeneous resources and communication protocols, in support of low-latency real-time streaming applications. As part of this framework, an application description language was developed and implemented. Using the application description language a number of high-order management modules were implemented including solutions for resource and workload comparison, performance observation, scheduling, and provisioning. A number of hypothetical and real-world use cases are described to support the framework implementation

Interoperability and Information Brokers in Public Safety: An Approach toward Seamless Emergency Communications

When a disaster occurs, the rapid gathering and sharing of crucial information among public safety agencies, emergency response units, and the public can save lives and reduce the scope of the problem; yet, this is seldom achieved. The lack of interoperability hinders effective collaboration across organizational and jurisdictional boundaries. In this article, we propose a general architecture for emergency communications that incorporates (1) an information broker, (2) events and event-driven processes, and (3) interoperability. This general architecture addresses the question of how an information broker can overcome obstacles, breach boundaries for seamless communication, and empower the public to become active participants in emergency communications. Our research is based on qualitative case studies on emergency communications, workshops with public safety agencies, and a comparative analysis of interoperability issues in the European public sector. This article features a conceptual approach toward proposing a way in which public safety agencies can achieve optimal interoperability and thereby enable seamless communication and crowdsourcing in emergency prevention and response

From data to applications in the Internet of Things

Con la crescita in complessità delle infrastrutture IT e la pervasività degli scenari di Internet of Things (IoT) emerge il bisogno di nuovi modelli computazionali basati su entità autonome capaci di portare a termine obiettivi di alto livello interagendo tra loro grazie al supporto di infrastrutture come il Fog Computing, per la vicinanza alle sorgenti dei dati, e del Cloud Computing per offrire servizi analitici complessi di back-end in grado di fornire risultati per milioni di utenti. Questi nuovi scenarii portano a ripensare il modo in cui il software viene progettato e sviluppato in una prospettiva agile. Le attività dei team di sviluppatori (Dev) dovrebbero essere strettamente legate alle attività dei team che supportano il Cloud (Ops) secondo nuove metodologie oggi note come DevOps. Tuttavia, data la mancanza di astrazioni adeguata a livello di linguaggio di programmazione, gli sviluppatori IoT sono spesso indotti a seguire approcci di sviluppo bottom-up che spesso risulta non adeguato ad affrontare la compessità delle applicazione del settore e l'eterogeneità dei compomenti software che le formano. Poichè le applicazioni monolitiche del passato appaiono difficilmente scalabili e gestibili in un ambiente Cloud con molteplici utenti, molti ritengono necessaria l'adozione di un nuovo stile architetturale, in cui un'applicazione dovrebbe essere vista come una composizione di micro-servizi, ciascuno dedicato a uno specifica funzionalità applicativa e ciascuno sotto la responsabilità di un piccolo team di sviluppatori, dall'analisi del problema al deployment e al management. Poichè al momento non si è ancora giunti a una definizione univoca e condivisa dei microservices e di altri concetti che emergono da IoT e dal Cloud, nè tantomento alla definzione di linguaggi sepcializzati per questo settore, la definzione di metamodelli custom associati alla produzione automatica del software di raccordo con le infrastrutture potrebbe aiutare un team di sviluppo ad elevare il livello di astrazione, incapsulando in una software factory aziendale i dettagli implementativi. Grazie a sistemi di produzione del sofware basati sul Model Driven Software Development (MDSD), l'approccio top-down attualmente carente può essere recuperato, permettendo di focalizzare l'attenzione sulla business logic delle applicazioni. Nella tesi viene mostrato un esempio di questo possibile approccio, partendo dall'idea che un'applicazione IoT sia in primo luogo un sistema software distribuito in cui l'interazione tra componenti attivi (modellati come attori) gioca un ruolo fondamentale