Device discovery in D2D communication: A survey

Device to Device (D2D) communication was first considered in out-band to manage energy issues in the wireless sensor networks. The primary target was to secure information about system topology for successive communication. Now the D2D communication has been legitimated in in-band by the 3rd Generation Partnership Project (3GPP). To initiate D2D communication, Device Discovery (DD) is a primary task and every D2D application benefits from DD as an end to end link maintenance and data relay when the direct path is obstructed. The DD is facing new difficulties because of the mobility of the devices over static systems, and the mobility makes it more challenging for D2D communication. For in-band D2D, DD in a single cell and multi-cell, and dense area is not legitimated properly, causing latency, inaccuracy, and energy consumption. Among extensive studies on limiting energy consumption and latency, DD is one of the essential parts concentrating on access and communication. In this paper, a comprehensive survey on DD challenges, for example single cell/multi-cell and dense area DD, energy consumption during discovery, discovery delay, and discovery security, etc., has been presented to accomplish an effective paradigm of D2D networks. In order to undertake the device (user) needs, an architecture has been projected, which promises to overwhelm the various implementation challenges of DD. The paper mainly focuses on DD taxonomy and classification with an emphasis on discovery procedures and algorithms, a summary of advances and issues, and ways for potential enhancements. For ensuring a secure DD and D2D, auspicious research directions have been proposed, based on taxonomy

Adaptive and autonomous protocol for spectrum identification and coordination in ad hoc cognitive radio network

The decentralised structure of wireless Ad hoc networks makes them most appropriate for quick and easy deployment in military and emergency situations. Consequently, in this thesis, special interest is given to this form of network. Cognitive Radio (CR) is defined as a radio, capable of identifying its spectral environment and able to optimally adjust its transmission parameters to achieve interference free communication channel. In a CR system, Dynamic Spectrum Access (DSA) is made feasible. CR has been proposed as a candidate solution to the challenge of spectrum scarcity. CR works to solve this challenge by providing DSA to unlicensed (secondary) users. The introduction of this new and efficient spectrum management technique, the DSA, has however, opened up some challenges in this wireless Ad hoc Network of interest; the Cognitive Radio Ad Hoc Network (CRAHN). These challenges, which form the specific focus of this thesis are as follows: First, the poor performance of the existing spectrum sensing techniques in low Signal to Noise Ratio (SNR) conditions. Secondly the lack of a central coordination entity for spectrum allocation and information exchange in the CRAHN. Lastly, the existing Medium Access Control (MAC) Protocol such as the 802.11 was designed for both homogeneous spectrum usage and static spectrum allocation technique. Consequently, this thesis addresses these challenges by first developing an algorithm comprising of the Wavelet-based Scale Space Filtering (WSSF) algorithm and the Otsu's multi-threshold algorithm to form an Adaptive and Autonomous WaveletBased Scale Space Filter (AWSSF) for Primary User (PU) sensing in CR. These combined algorithms produced an enhanced algorithm that improves detection in low SNR conditions when compared to the performance of EDs and other spectrum sensing techniques in the literature. Therefore, the AWSSF met the performance requirement of the IEEE 802.22 standard as compared to other approaches and thus considered viable for application in CR. Next, a new approach for the selection of control channel in CRAHN environment using the Ant Colony System (ACS) was proposed. The algorithm reduces the complex objective of selecting control channel from an overtly large spectrum space,to a path finding problem in a graph. We use pheromone trails, proportional to channel reward, which are computed based on received signal strength and channel availability, to guide the construction of selection scheme. Simulation results revealed ACS as a feasible solution for optimal dynamic control channel selection. Finally, a new channel hopping algorithm for the selection of a control channel in CRAHN was presented. This adopted the use of the bio-mimicry concept to develop a swarm intelligence based mechanism. This mechanism guides nodes to select a common control channel within a bounded time for the purpose of establishing communication. Closed form expressions for the upper bound of the time to rendezvous (TTR) and Expected TTR (ETTR) on a common control channel were derived for various network scenarios. The algorithm further provides improved performance in comparison to the Jump-Stay and Enhanced Jump-Stay Rendezvous Algorithms. We also provided simulation results to validate our claim of improved TTR. Based on the results obtained, it was concluded that the proposed system contributes positively to the ongoing research in CRAHN

Distributed Cognition as the Basis for Adaptation and Homeostasis in Robots

Many researchers approach the problem of building autonomous systems by looking to biology for inspiration. This has given rise to a wide-range of artificial systems mimicking their biological counterparts—artificial neural networks, artificial endocrine systems, and artificial musculoskeletal systems are prime examples. While these systems are succinct and work well in isolation, they can become cumbersome and complicated when combined to perform more complex tasks. Autonomous behaviour is one such complex task. This thesis considers autonomy as the complex behaviour it is, and proposes a bottom-up approach to developing autonomous behaviour from cognition. This consists of investigating how cognition can provide new approaches to the current limitations of swarm systems, and using this as the basis for one type of autonomous behaviour: artificial homeostasis. Distributed cognition, a form of emergent cognition, is most often described in terms of the immune system and social insects. By taking inspiration from distributed cognition, this thesis details the development of novel algorithms for cognitive decision-making and emergent identity in leaderless, homogenous swarms. Artificial homeostasis is provided to a robot through an architecture that combines the cognitive decision-making algorithm with a simple associative memory. This architecture is used to demonstrate how a simple architecture can endow a robot with the capacity to adapt to an unseen environment, and use that information to proactively seek out what it needs from the environment in order to maintain its internal state

A framework for engineering reusable self-adaptive systems

The increasing complexity and size of information systems result in an increasing effort for maintenance. Additionally, miniaturization of devices leads to mobility and the need for context-adaptation. Self-adaptive Systems (SASs) can adapt to changes in their environment or the system itself. So far, however, development of SASs is frequently tailored towards the requirements of use cases. The research for reusable elements — for implementation as well as design processes — is often neglected. Integrating reusable processes and implementation artifacts into a framework and offering a tool suite to developers would make development of SASs faster and less error-prone. This thesis presents the Framework for Engineering Self-adaptive Systems (FESAS). It offers a reusable implementation of a reference system, tools for implementation and design as well as a middleware for controlling system deployment. As a second contribution, this thesis introduces a new approach for self-improvement of SASs which complements the SAS with meta-adaptation

Task Allocation in Foraging Robot Swarms:The Role of Information Sharing

Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms

Recent Development of Hybrid Renewable Energy Systems

Abstract: The use of renewable energies continues to increase. However, the energy obtained from renewable resources is variable over time. The amount of energy produced from the renewable energy sources (RES) over time depends on the meteorological conditions of the region chosen, the season, the relief, etc. So, variable power and nonguaranteed energy produced by renewable sources implies intermittence of the grid. The key lies in supply sources integrated to a hybrid system (HS)