Superstring: A scalable service discovery protocol for the wide-area pervasive environment

Arguably, the world has become one large pervasive computing environment. Our planet is growing a digital skin of a wide array of sensors, hand-held computers, mobile phones, laptops, web services and publicly accessible web-cams. Often, these devices and services are deployed in groups, forming small communities of interacting devices. Service discovery protocols allow processes executing on each device to discover services offered by other devices within the community. These communities can be linked together to form a wide-area pervasive environment, allowing processes in one p u p tu interact with services in another. However, the costs of communication and the protocols by which this communication is mediated in the wide-area differ from those of intra-group, or local-area, communication. Communication is an expensive operation for small, battery powered devices, but it is less expensive for servem and workstations, which have a constant power supply and 81'e connected to high bandwidth networks. This paper introduces Superstring, a peer to-peer service discovery protocol optimised fur use in the wide-area. Its goals are to minimise computation and memory overhead in the face of large numbers of resources. It achieves this memory and computation scalability by distributing the storage cost of service descriptions and the computation cost of queries over multiple resolvers.

Vertical handovers as adaption methods in pervasive systems

Pervasive systems need to be context aware and need to adapt to context changes, including network disconnections and changes in network Quality of Service (QoS). Vertical handover (handover between heterogeneous networks) is one of possible adaptation methods. It allows users to roam freely between heterogeneous networks while maintaining continuity of their applications. This paper proposes a vertical handover approach suitable for multimedia applications in pervasive systems. It describes the adaptability decision making process which uses vertical handovers to support users mobility and provision of QoS suitable for users’ applications. The process evaluates context information regarding user devices, User location, network environment, and user perceived QoS of applications.

Role of information and communication in redefining unmanned aerial vehicle autonomous control levels

Autonomous mission control, unlike automatic mission control which is generally pre-programmed to execute an intended mission, is guided by the philosophy of carrying out a complete mission on its own through online sensing, information processing, and control reconfiguration. A crucial cornerstone of this philosophy is the capability of intelligence and of information sharing between unmanned aerial vehicles (UAVs) or with a central controller through secured communication links. Though several mission control algorithms, for single and multiple UAVs, have been discussed in the literature, they lack a clear definition of the various autonomous mission control levels. In the conventional system, the ground pilot issues the flight and mission control command to a UAV through a command data link and the UAV transmits intelligence information, back to the ground pilot through a communication link. Thus, the success of the mission depends entirely on the information flow through a secured communication link between ground pilot and the UAV In the past, mission success depended on the continuous interaction of ground pilot with a single UAV, while present day applications are attempting to define mission success through efficient interaction of ground pilot with multiple UAVs. However, the current trend in UAV applications is expected to lead to a futuristic scenario where mission success would depend only on interaction among UAV groups with no interaction with any ground entity. However, to reach this capability level, it is necessary to first understand the various levels of autonomy and the crucial role that information and communication plays in making these autonomy levels possible. This article presents a detailed framework of UAV autonomous mission control levels in the context of information flow and communication between UAVs and UAV groups for each level of autonomy.