  In this paper, we propose DSSP, a Distributed, SLO-aware,
Sensing-domain-privacy-Preserving architecture for Sensing-as-a-Service (SaS).
DSSP addresses four major limitations of the current SaS architecture. First,
to improve sensing quality and enhance geographic coverage, DSSP allows
Independent sensing Administrative Domains (IADs) to participate in sensing
services, while preserving the autonomy of control and privacy for individual
domains. Second, DSSP enables a marketplace in which a sensing data seller
(i.e., an IAD) can sell its sensing data to more than one buyer (i.e., cloud
service provider (CSP)), rather than being locked in with just one CSP. Third,
DSSP enables per-query tail-latency service-level-objective (SLO) guaranteed
SaS. Fourth, DSSP enables distributed, rather than centralized, query
scheduling, making SaS highly scalable. At the core of DSSP is the design of a
budget decomposition technique that translates: (a) a query tail-latency SLO
into exact task response time budgets for sensing tasks of the query dispatched
to individual IADs; and (b) the task budget for a task arrived at an IAD into
exact subtask queuing deadlines for subtasks of the task dispatched to
individual edge nodes in each IAD. This enables IADs to allocate their internal
resources independently and accurately to meet the task budgets and hence,
query tail-latency SLO, based on a simple subtask-budget-aware
earliest-deadline-first queuing (EDFQ) policy for all the subtasks. The
performance and scalability of DSSP are evaluated and verified by both
on-campus testbed experiment at small scale and simulation at large scale.
