Hitch Hiker 2.0: a binding model with flexible data aggregation for the Internet-of-Things

Wireless communication plays a critical role in determining the lifetime of Internet-of-Things (IoT) systems. Data aggregation approaches have been widely used to enhance the performance of IoT applications. Such approaches reduce the number of packets that are transmitted by combining multiple packets into one transmission unit, thereby minimising energy consumption, collisions and congestion. However, current data aggregation schemes restrict developers to a specific network structure or cannot handle multi-hop data aggregation. In this paper, we propose Hitch Hiker 2.0, a component binding model that provides support for multi-hop data aggregation. Hitch Hiker uses component meta-data to discover remote component bindings and to construct a multi-hop overlay network within the free payload space of existing traffic flows. Hitch Hiker 2.0 provides end-to-end routing of low-priority traffic while using only a small fraction of the energy of standard communication. This paper extends upon our previous work by incorporating new mechanisms for decentralised route discovery and providing additional application case studies and evaluation. We have developed a prototype implementation of Hitch Hiker for the LooCI component model. Our evaluation shows that Hitch Hiker consumes minimal resources and that using Hitch Hiker to deliver low-priority traffic reduces energy consumption by up to 32 %

Distributed Service Discovery for Heterogeneous Wireless Sensor Networks

Service discovery in heterogeneous Wireless Sensor Networks is a challenging research objective, due to the inherent limitations of sensor nodes and their extensive and dense deployment. The protocols proposed for ad hoc networks are too heavy for sensor environments. This paper presents a resourceaware solution for the service discovery problem, which exploits the heterogeneous nature of the sensor network and alleviates the high-density problem from the flood-based approaches. The idea is to organize nodes into clusters, based on the available resources and the dynamics of nodes. The clusterhead nodes act as a distributed directory of service registrations. Service discovery messages are exchanged among the nodes in the distributed directory. The simulation results show the performance of the service discovery protocol in heterogeneous dense environments

Transmit power control and data rate enhancement in cognitive radio network using computational intelligence

Underutilized radio frequencies are the chief apprehension in advance radio communication. The radio recourses are sparse and costly and their efficient allocation has become a challenge. Cognitive radio networks are the ray of hope. Cognitive radio networks use dynamic spectrum access technique to opportunistically retrieve and share the licensed spectrum. The licensed users are called primary users and the users that opportunistically access the licensed spectrum all called secondary users. The proposed system is a feedback system that work on demand and supply concept, in which secondary receivers senses the vacant spectrum and shares the information with the secondary transmitters. The secondary transmitters adjust their transmission parameters of transmit power and data rate in such a way that date rate is maximized. Two methods of spectrum access using frequency division multiple access (FDMA) and Time division multiple access (TDMA) are discussed. Interference temperature limit and maximum achievable capacity are the constraints that regulate the entire technique. The aim of the technique is to control the transmitter power according to the data requirements of each secondary user and optimizing the resources like bandwidth, transmit power using machine learning and feed forward back propagation deep neural networks making full use of the network capacity without hampering the operation of primary network

A framework for proving the self-organization of dynamic systems

This paper aims at providing a rigorous definition of self- organization, one of the most desired properties for dynamic systems (e.g., peer-to-peer systems, sensor networks, cooperative robotics, or ad-hoc networks). We characterize different classes of self-organization through liveness and safety properties that both capture information re- garding the system entropy. We illustrate these classes through study cases. The first ones are two representative P2P overlays (CAN and Pas- try) and the others are specific implementations of \Omega (the leader oracle) and one-shot query abstractions for dynamic settings. Our study aims at understanding the limits and respective power of existing self-organized protocols and lays the basis of designing robust algorithm for dynamic systems

Kompics: a message-passing component model for building distributed systems

The Kompics component model and programming framework was designedto simplify the development of increasingly complex distributed systems. Systems built with Kompics leverage multi-core machines out of the box and they can be dynamically reconfigured to support hot software upgrades. A simulation framework enables deterministic debugging and reproducible performance evaluation of unmodified Kompics distributed systems. We describe the component model and show how to program and compose event-based distributed systems. We present the architectural patterns and abstractions that Kompics facilitates and we highlight a case study of a complex distributed middleware that we have built with Kompics. We show how our approach enables systematic development and evaluation of large-scale and dynamic distributed systems

Cross-layer Peer-to-Peer Computing in Mobile Ad Hoc Networks

The future information society is expected to rely heavily on wireless technology. Mobile access to the Internet is steadily gaining ground, and could easily end up exceeding the number of connections from the fixed infrastructure. Picking just one example, ad hoc networking is a new paradigm of wireless communication for mobile devices. Initially, ad hoc networking targeted at military applications as well as stretching the access to the Internet beyond one wireless hop. As a matter of fact, it is now expected to be employed in a variety of civilian applications. For this reason, the issue of how to make these systems working efficiently keeps the ad hoc research community active on topics ranging from wireless technologies to networking and application systems. In contrast to traditional wire-line and wireless networks, ad hoc networks are expected to operate in an environment in which some or all the nodes are mobile, and might suddenly disappear from, or show up in, the network. The lack of any centralized point, leads to the necessity of distributing application services and responsibilities to all available nodes in the network, making the task of developing and deploying application a hard task, and highlighting the necessity of suitable middleware platforms. This thesis studies the properties and performance of peer-to-peer overlay management algorithms, employing them as communication layers in data sharing oriented middleware platforms. The work primarily develops from the observation that efficient overlays have to be aware of the physical network topology, in order to reduce (or avoid) negative impacts of application layer traffic on the network functioning. We argue that cross-layer cooperation between overlay management algorithms and the underlying layer-3 status and protocols, represents a viable alternative to engineer effective decentralized communication layers, or eventually re-engineer existing ones to foster the interconnection of ad hoc networks with Internet infrastructures. The presented approach is twofold. Firstly, we present an innovative network stack component that supports, at an OS level, the realization of cross-layer protocol interactions. Secondly, we exploit cross-layering to optimize overlay management algorithms in unstructured, structured, and publish/subscribe platforms

Cloud Computing and Cloud Automata as A New Paradigm for Computation

Cloud computing addresses how to make right resources available to right computation to improve scaling, resiliency and efficiency of the computation. We argue that cloud computing indeed, is a new paradigm for computation with a higher order of artificial intelligence (AI), and put forward cloud automata as a new model for computation. A high-level AI requires infusing features that mimic human functioning into AI systems. One of the central features is that humans learn all the time and the learning is incremental. Consequently, for AI, we need to use computational models, which reflect incremental learning without stopping (sentience). These features are inherent in reflexive, inductive and limit Turing machines. To construct cloud automata, we use the mathematical theory of Oracles, which include Oracles of Turing machines as its special case. We develop a hierarchical approach based on Oracles with different ranks that includes Oracle AI as a special case. Discussing a named-set approach, we describe an implementation of a high-performance edge cloud using hierarchical name-oriented networking and Oracle AI-based orchestration. We demonstrate how cloud automata with a control overlay allows microservice network provisioning, monitoring and reconfiguration to address non-deterministic fluctuations affecting their behavior without interrupting the overall evolution of computation