Accurate, reliable and actionable intelligence produced by smart environment assets is essential in order for dynamic operations such as emergency response or humanitarian relief to be effective. Leveraging Future Internet building block technologies, smart environments are ecosystems which seamlessly embed IT assets into physical world's objects that collect, process and disseminate operational data and insights. The management of smart environment assets towards an efficient collaboration in multi-partner, dynamic scenarios where assets are owned and operated by different partners is a non-trivial problem, due to partners' restrictive sharing policies. In this work we compare two asset sharing approaches; the first is based on a traditional asset ownership model, while the second novel one, is based on an edge team-based model where users are grouped into cross-partner teams and as team members they have access to assets belonging to all the partners participating in team. We further experiment with the second, unexplored team-based sharing model by testing its behavior under different user mobility patterns and extreme asset ownership models investigating its impact on MSTA-P, a policy-regulated version of an existing asset-task assignment protocol. For the protocol's evaluation we implement a multi-partner operation scenario using an open source, agent-based and discrete time simulation environment.

Smart environments are ecosystems, which seamlessly embed IT assets into physical world's objects and hold promise for improving the services we receive from our social and economic ecosystems. The management of smart environment assets in multi-partner, dynamic collaboration scenarios where different sets of assets are owned and operated by different partners is a non-trivial problem, due to restrictive asset sharing policies applied by collaborating partners. In this work we formalize, evaluate and compare two asset sharing policies, investigating their impact on MSTA-P, a policy-regulated version of an existing asset-task assignment protocol. The first sharing policy is based on a traditional asset ownership model while the second is based on an edge model allowing asset sharing among collaborating partnes through cross-partner team formations. We find that while the traditional ownership model allows slightly better performance, the difference is only marginal, so a team-sharing model offers a viable alternative sharing approach.

Support for intelligent and autonomous resource management is one key factor to the success of modern sensor network systems. The limited resources, such as exhaustible battery life, moderate processing ability and finite bandwidth, restrict the system's ability to serve multiple users simultaneously. It always happens that only a subset of tasks is selected with the goal of maximizing total profit. Besides, because of uncertain factors like unreliable wireless medium or variable quality of sensor outputs, it is not practical to assume that both demands and profits of tasks are deterministic and known a priori, both of which may be stochastic following certain distributions. In this paper, we model this resource allocation challenge as a stochastic knapsack problem. We study a specific case in which both demands and profits follow normal distributions, which are then extended to Poisson and Binomial variables. A couple of tunable parameters are introduced to configure two probabilities: one limits the capacity overflow rate with which the combined demand is allowed to exceed the available supply, and the other sets the minimum chance at which expected profit is required to be achieved. We define relative values for random variables in given conditions, and utilize them to search for the best resource allocation solutions. We propose heuristics with different optimality/efficiency tradeoffs, and find that our algorithms run relatively fast and provide results considerably close to the optimum.

When a sensor network is deployed in the field it is typically required to support multiple simultaneous missions, which may start and finish at different times. Schemes that match sensor resources to mission demands thus become necessary. In this paper, we consider new sensor-assignment problems motivated by frugality, i.e., the conservation of resources, for both static and dynamic settings. In the most general setting, the problems we study are NP-hard even to approximate, and so we focus on heuristic algorithms that perform well in practice. In the static setting, we propose a greedy centralized solution and a more sophisticated solution that uses the Generalized Assignment Problem model and can be implemented in a distributed fashion. In what we call the dynamic setting, missions arrive over time and have different durations. For this setting, we give heuristic algorithms in which available sensors propose to nearby missions as they arrive. We find that the overall performance can be significantly improved if available sensors sometimes refuse to offer utility to missions they could help, making this decision based on the value of the mission, the sensor's remaining energy, and (if known) the remaining target lifetime of the network. Finally, we evaluate our solutions through simulations.