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To ensure that a multi-hop cluster of batterypowered, wireless sensor motes can complete all of its tasks, each task must minimize its use of communication and processing resources. For event detection tasks that are subject to both measurement errors by sensors and communication errors in the wireless channel, this implies that: (i) the Cluster-Head (CH) must optimally fuse the decisions received from its cluster in order to reduce the effect of measurement errors; (ii) the CH and all motes that relay other motes' decisions must adopt lowcomplexity processing and coding algorithms that minimize the effects of communication errors. This paper combines a Maximum a Posteriori (MAP) approach for local and global decisions in multi-hop sensor networks with low-complexity repetition codes and processing algorithms. It is shown by analysis and confirmed by simulation that there exists an odd integer M and an integer KM such the decision error probability at the CH is reduced when: (1) nodes in rings k ≤ KM hops from the CH directly relay their decisions to the CH; (2) nodes in rings k > KM locally fuse groups of M decisions and then use a repetition code to forward these fused decisions to the CH; and (3) KM is a nondecreasing function of M. This algorithm - and hybrid, hierarchical, and compression approaches based on it - enable tradeoffs amongst the probability of error, energy usage, compression ratio, complexity, and time to decision.