As one of the wireless sensor network killer applications, object tracking sensor networks (OTSNs) disclose many opportunities for energy-aware system design and implementations. We investigate prediction-based approaches for performing energy efficient reporting in OTSNs. We propose a dual prediction-based reporting mechanism (called DPR), in which both sensor nodes and the base station predict the future movements of the mobile objects. Transmissions of sensor readings are avoided as long as the predictions are consistent with the real object movements. DPR achieves energy efficiency by intelligently trading off multihop/long-range transmissions of sensor readings between sensor nodes and the base station with one-hop/short-range communications of object movement history among neighbor sensor nodes. We explore the impact of several system parameters and moving behavior of tracked objects on DPR performance, and also study two major components of DPR: prediction models and location models through simulations. Our experimental results show that DPR is able to achieve considerable energy savings under various conditions and outperforms existing reporting mechanisms.

Prefetching is a technique for lowering the access delay by making data available in anticipation of future requests. Correspondingly, power control has been proposed in wireless networks for the efficient use of the wireless resources and low energy consumption at the transmitters. This work investigates the two techniques jointly (1) for communications over a fluctuating wireless channel whose dynamics and statistics is unknown, and (2) explore approximating schemes for exercising deep prefetching. In short, a user uses a wireless terminal to access various data items residing at a server over a wireless network. Every requested item not found in the cache of the terminal incurs to the system (1) an access delay cost, and (2) an energy/network cost to download it over the wireless link. To minimize the total cost, the system may either (i) postpone the transmissions when the link quality is sensed to be low, or reversely, (ii) proactively prefetch data items during link quality 'highs', in anticipation to future user requests. The decision therefore involves choosing when and what to (pre)fetch, and at what power level. To quantify on the above, we formulate the problem in the context of controlled Markov chains using the technique of dynamic programming. After analyzing the structure of the problem, we construct a set of policies based on justified heuristics for taking near-to-optimal decisions. Simulation is then used to quantify on the performance gains over standard schemes.

Mobile and ubiquitous computing emphasize the need for highly adaptive delivery of Internet services. While several systems and even products exist that guarantee shallow or deep adaptation, they are usually based on profile information and customer relationship management modules stored and operating at the service provider. On the contrary, We assume that profile information, including user personal data and preferences, device capabilities, network bandwidth, location and other contextual information, as well as policy rules that can dynamically change this data, are provided by different sources. We describe a formal framework for aggregating this information and for solving conflicts between policy rules. We provide both a theoretical study of the properties of our techniques and a practical evaluation study obtained through a prototype implementation.

We present a novel technique to combine services, provided by mobile as well as fixed devices, on-the-fly and in a context-sensitive way within smart environments containing groups of people. The key intuition at the base of our approach is that, in those environments, similar functions are provided simultaneously by many agents on board of different devices; the issue becomes then of combining and coordinating their services in order to exploit their aggregated power or improve delivery. At the core of our work is the idea of implicit organizations, that is, teams of agents that continuously renegotiate how to provide services while the environment evolves. A concept demonstrator shows how implicit organizations can support a smart virtual meeting room. We foresee the application of our approach in assisting users and groups of users in active environments, such as an active museum. Here, our approach can enhance knowledge transfer and communication for groups visiting together cultural exhibits.

In a CDMA2000 network, a mobile node (MN) gets packet data service by establishing a PPP session with a packet data serving node (PDSN). The PDSN acts as the muter for the IP packets transported over the PPP session. A packet control function (PCF) sits between the radio access network and the packet network, selects the PDSN for the MN during session setup, and relays the PPP frames between the MN and the PDSN during the session. The PCF selects a PDSN based on the MN's device identity. Due to this, the data services that a mobile user subscribes become tightly coupled with the device that the user registers with the service provider. Because the PCF selects a PDSN based only on mobile device identity, it is unable to select the "best" PDSN to support the services subscribed by a specific user. This work presents the design and implementation of an entity called radio-packet session redirector (RPSR) that works within the current standard and alleviates this shortcoming. The RPSR intercepts a PPP session, parses the user identity, selects a PDSN based on the user identity, creates a PPP session with the selected PDSN and splices the sessions together so that PPP frames can be seamlessly forwarded between the MN and the PDSN with very little overhead. We identify a number of services that RPSR enables in the network. We describe a prototype stand-alone device implementation of the RPSR in the Linux kernel and present performance results.