A prototype for WPAN slicing and virtualization is showcased in this demo, where we form a contiguous wireless PAN "underlay" spanned over two neighboring IEEE 802.15.4 mote networks.

Nodes in mobile ad hoc networks have limited transmission ranges that necessitate multihop communication. Hence the nodes expect their neighbours to relay the packets meant for nodes out of the transmission range of the source. Ad hoc networks are based on the fundamental assumption that if a node promises to relay a packet, it will relay the packet and will not cheat. This assumption becomes invalid when the nodes in the network have contradictory goals. As a result, routing protocols for ad hoc networks become vulnerable to rogue nodes. The reputations of the intermediate nodes, based on their past history of relaying packets, can be used by their neighbours to ensure that the packet will be relayed by the intermediate nodes. This paper introduces a reputation scheme for ad hoc networks that can motivate the intermediate nodes to relay packets. The source performs a route discovery (using Ad hoc on Demand Distance Vector Routing Protocol (AODV)) and finds a set of routes to the destination. Instead of choosing the shortest route to the destination, the source node chooses a path whose next hop node has the highest reputation. This policy, when used recursively, in the presence of 40% rogue nodes, improves the throughput of the system to 65%, from the 22% throughput provided by AODV with same number of rogue nodes. This improvement is obtained at the cost of a higher number of route discoveries with a minimal increase in the average hop length.

The rapid advances in a wide range of wireless access technologies along with the efficient use of smart spaces have already set the stage for the development of smart homes. Context-awareness is perhaps the most salient feature in these intelligent computing platforms. The "location" information of the users plays a vital role in defining this context. To extract the best performance and efficacy of such smart computing environments, one needs a scalable, technology-independent location service. We have developed a predictive framework for location-aware resource optimization in smart homes. The underlying compression mechanism helps in efficient learning of an inhabitant's movement (location) profiles in the symbolic domain. The concept of Asymptotic Equipartition Property (AEP) in information theory helps to predict the inhabitant's future location as well as most likely path-segments with good accuracy. Successful prediction helps in pro-active resource management and on-demand operations of automated devices along the inhabitant's future paths and locations - thus providing the necessary comfort at a near-optimal cost. Simulation results on a typical smart home floor plans corroborate this high prediction success and demonstrate sufficient reduction in daily energy-consumption, manual operations and time spent by the inhabitant which are considered as a fair measure of his/her comfort.

The convergence of wired/wireless telecommunication networks and IP-based data networks to form a seamless global personal communication system has set up the stage for a network independent universal location service. Symbolic representation of location space in terms of cells is the key to its viability. Under this framework, universal learning, estimation and prediction of personal mobility is still possible for well-behaved users, in spite of the fact that no single mobility model works for the diverse set of wireless infrastructures.

The complexity of the mobility tracking problem in a cellular environment has been characterized under an informationtheoretic framework.Shannon's entropy measure is identified as a basis for comparing user mobility models.By building and maintaining a dictionary of individual user's path updates (as opposed to the widely used location updates), the proposed adaptive on-line algorithm can learn subscribers' profiles.This technique evolves out of the concepts of lossless compression.The compressibility of the variable-to-fixed length encoding of the acclaimed Lempel-Ziv family of algorithms reduces the update cost, whereas their built-in predictive power can be effectively used to reduce paging cost.