In　recent　years,　significant　research　efforts　have　been　spent　towards　building　intelligent　and　user-friendly　IoT　systems　to　enable　a　new　generation　of　applications　capable　of　performing　complex　sensing　and　recognition　tasks.　In　many　of　such　applications,　there　are　usually　multiple　different　sensors　monitoring　the　same　object.　Each　of　these　sensors　can　be　regarded　as　an　information　source　and　provides　us　a　unique　＂view＂　of　the　observed　object.　Intuitively,　if　we　can　combine　the　complementary　information　carried　by　multiple　sensors,　we　will　be　able　to　improve　the　sensing　performance.　Towards　this　end,　we　propose　DeepFusion,　a　unified　multi-sensor　deep　learning　framework,　to　learn　informative　representations　of　heterogeneous　sensory　data.　DeepFusion　can　combine　different　sensors＇　information　weighted　by　the　quality　of　their　data　and　incorporate　cross-sensor　correlations,　and　thus　can　benefit　a　wide　spectrum　of　IoT　applications.　To　evaluate　the　proposed　DeepFusion　model,　we　set　up　two　real-world　human　activity　recognition　testbeds　using　commercialized　wearable　and　wireless　sensing　devices.　Experiment　results　show　that　DeepFusion　can　outperform　the　state-of-the-art　human　activity　recognition　methods.