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In the Internet of Things (IoT) environment, edge computing can be initiated at anytime and anywhere. However, in an IoT environment, edge computing sessions are often ephemeral, i.e., they last for a short period of time and can often be discontinued once the current application usage is completed or the edge devices leave the system due to factors such as mobility. Therefore, in this paper, the problem of ephemeral edge computing in an IoT is studied by considering scenarios in which edge computing operates within a limited time period. To this end, a novel online framework is proposed in which a source edge node offloads its computing tasks from sensors within an area to neighboring edge nodes for distributed task computing, within the limited period of time of an ephemeral edge computing system. The online nature of the framework allows the edge nodes to optimize their task allocation and decide on which neighbors to use for task processing, even when the tasks are revealed to the source edge node in an online manner, and the information on future task arrivals is unknown. The proposed framework essentially maximizes the number of computed tasks by jointly considering the communication and computation latency. To solve the joint optimization, an online greedy algorithm is proposed and solved by using the primal-dual approach. Since the primal problem provides an upper bound of the original dual problem, the competitive ratio of the online approach is analytically derived as a function of the task sizes and the data rates of the edge nodes. Simulation results show that the proposed online algorithm can achieve a near-optimal task allocation with an optimality gap that is no higher than 7.1% compared to the offline, optimal solution with complete knowledge of all tasks.

This study examines the determinants of a firm’s generative appropriation, defined as a firms’ effectiveness in capturing a larger share of cumulative inventions spawned from its prior inventions. Building on learning theory and the resource-based view, we investigate how a firm’s knowledge diversity across technological and geographic space influences generative appropriation. We also investigate how technology opportunities moderate the effect of knowledge diversity on generative appropriation. Using a longitudinal study of US manufacturing firms and using patents as a proxy for inventions, we find that technological knowledge diversity has a negative effect on generative appropriation and that technology opportunity weakens that effect. We also find that the effect of geographic diversity is contingent on the availability of technological opportunities. When technology opportunity is low, moderate geographic diversity leads to higher generative appropriation than high or low geographic diversity does. However, when technology opportunity is high, geographic diversity appears to have a monotonically negative effect on generative appropriation. These results highlight the way the cumulative and preclusive learning of prior knowledge depend on a firm’s knowledge diversity and technology opportunities.