Autonomic communications seek to improve the ability of network and services to cope with unpredicted change, including changes in topology, load, task, the physical and logical characteristics of the networks that can be accessed, and so forth. Broad-ranging autonomic solutions require designers to account for a range of end-to-end issues affecting programming models, network and contextual modeling and reasoning, decentralised algorithms, trust acquisition and maintenance---issues whose solutions may draw on approaches and results from a surprisingly broad range of disciplines. We survey the current state of autonomic communications research and identify significant emerging trends and techniques.

Unmanned aerial vehicles, or drones, are already an integral part of the equipment used by firefighters to monitor wildfires. They are, however, still typically used only as remotely operated, mobile sensing platforms under direct real-time control of a human pilot. Meanwhile, a substantial body of literature exists that emphasises the potential of autonomous drone swarms in various situational awareness missions, including in the context of environmental protection. In this paper, we present the results of a systematic investigation by means of numerical methods i.e., Monte Carlo simulation. We report our insights into the influence of key parameters such as fire propagation dynamics, surface area under observation and swarm size over the performance of an autonomous drone force operating without human supervision. We limit the use of drones to perform passive sensing operations with the goal to provide real-time situational awareness to the fire fighters on the ground. Therefore, the objective is defined as being able to locate, and then establish a continuous perimeter (cordon) around, a simulated fire event to provide live data feeds such as e.g., video or infra-red. Special emphasis was put on exclusively using simple, robust and realistically implementable distributed decision functions capable of supporting the self-organisation of the swarm in the pursuit of the collective goal. Our results confirm the presence of strong nonlinear effects in the interaction between the aforementioned parameters, which can be closely approximated using an empirical law. These findings could inform the mobilisation of adequate resources on a case-by-case basis, depending on known mission characteristics and acceptable odds (chances of success).

With the growing reliability of modern ad hoc networks, it is encouraging to analyze the potential involvement of autonomous ad hoc agents in critical situations where human involvement could be perilous. One such critical scenario is the Search and Rescue effort in the event of a disaster, in which timely discovery and help deployment is of utmost importance. This paper demonstrates the applicability of a bio-inspired technique, namely Ant Algorithms (AA), in optimizing the search time for a route or path to a trapped victim, followed by the application of Dijkstra’s algorithm in the rescue phase. The inherent exploratory nature of AA is put to use for faster mapping and coverage of the unknown search space. Four different AA are implemented, with different effects of the pheromone in play. An inverted AA, with repulsive pheromones, was found to be the best fit for this particular application. After considerable exploration, upon discovery of the victim, the autonomous agents further facilitate the rescue process by forming a relay network, using the already deployed resources. Hence, the paper discusses a detailed decision-making model of the swarm, segmented into two primary phases that are responsible for the search and rescue, respectively. Different aspects of the performance of the agent swarm are analyzed as a function of the spatial dimensions, the complexity of the search space, the deployed search group size, and the signal permeability of the obstacles in the area.

A new paradigm of thinking pertaining to a novel holistic intelligent campus (iCampus) environment is proposed, in this paper, in order to enrich and enhance, as well as to transform, the end-to-end learning lifecycle of a knowledge ecosystem. Analogous to the different functions of a biological brain, the central digital nervous system of the campus is comprised of various different interconnected functional intelligences. Each of these intelligent areas is set to perform its specified functional role in a dynamic and coherent inter- and intra-integrative manner within the environment itself. A generalized roadmap has also been devised to encapsulate the concept from within an existing or a new campus setting. Note that the nature of the iCampus proposition is inherently multi-disciplinary and has multi-applicability to other forms of intelligent environment. To capture part of the essence of the concept, some of the key challenges pertinent to the iCampus ecosystem have also been highlighted within the campus value proposition framework.

Efficient Demand Response or Demand-Side Management (DSM) has long been one of the key aspirations of the Energy Sector. In Smart Grids, there could be many opportunities for semi-automated DSM, assuming proper coordination (i.e. de-synchronization) mechanisms are in place. In this paper, we present the results of a simulation study that was carried out to outline the potential benefits and sketch the key ICT requirements for DSM in Smart Grids. Our preliminary findings indicate that a simple, centralized reactive pricing scheme could be implemented with an acceptable communication overhead, but also suggest high computational needs.