This paper presents a secure and flexible process integration approach enabling distributed data fusion in military IoT applications. It seamlessly combines two recently developed technologies, the Dynamic Process Integration Framework and Martello, a Data Centric Security approach. The emphasis is on secure composition of heterogeneous services, corresponding to different types of algorithms as well as data sources that can be distributed over a system of networked computing nodes. The approach supports a relevant class of decision support functions that distil actionable information in environments characterized through dynamic constellations of sensors and computing services, limited communication bandwidth and stringent security requirements. Such functions contribute key elements of Information Driven Operation that can significantly improve the speed, quality and coverage of the OODA loops in challenging settings, such as Federated Mission Networks.

We present a tandem network of queues 0 , ⋯ , s- 1. Customers arrive at queue 0 according to a Poisson process with rate λ. There are s independent batch service processes at exponential rates μ₀, ⋯ , μ_{s - 1}. Service process i, i= 0 , ⋯ , s- 1 , at rate μ_i is such that all customers of all queues 0 , ⋯ , i simultaneously receive service and move to the next queue. We show that this system has a geometric product-form steady-state distribution. Moreover, we determine the service allocation that minimizes the waiting time in the system and state conditions to approximate such optimal allocations. Our model is motivated by applications in wireless sensor networks, where s observations from different sensors are collected for data fusion. We demonstrate that both optimal centralized and decentralized sensor scheduling can be modeled by our queueing model by choosing the values of μ_i appropriately. We quantify the performance gap between the centralized and decentralized schedules for arbitrarily large sensor networks.