Gun　violence　and　misuse　pose　great　threat　to　the　public　safety.　Real　time　monitoring　of　guns　usage　and　gunshot　events　are　very　promising　for　effective　gun　control.　However,　most　available　monitoring　systems　are　installed　in　fixed　location　instead　of　the　guns,　which　greatly　limits　the　flexibility　and　coverage.　In　this　study,　we　propose　a　wireless　gun　monitoring　and　gunshot　recognition　system　based　on　a　low-cost　triaxial　acceleration　sensor,　which　can　monitor　the　gun　in　real　time　and　accurately　recognize　gunshot　events.　Addressing　the　limited　resources　of　the　embedded　systems,　we　further　propose　an　efficient　gunshot　recognition　algorithm　EfficientNetTime　that　combines　the　lightweight　neural　network　and　knowledge　distillation,　so　as　to　enable　the　deployment　on　embedded　devices.　Firstly,　a　novel　lightweight　deep　learning　model　is　proposed　as　the　basic　model,　which　combines　the　advantages　of　one-dimensional　convolution　and　depthwise　separable　convolution　to　effectively　characterize　the　gunshot　signal　while　decreasing　the　computing　cost　of　convolution.　Secondly,　using　the　knowledge　distillation,　EfficientNetTime　is　used　as　the　teacher　model　to　generate　a　compressed　student　model　that　maintains　accuracy　and　greatly　reducing　model　size.　Lastly,　the　EfficientNetTime　student　model　can　be　deployed　on　resource-limited　embedded　systems.　The　proposed　method　can　automatically　extract　features　for　end-to-end　recognition　and　is　robust　to　temporal　transformations　of　input　signals.　Using　a　publicly　available　gunshot　dataset,　the　proposed　EfficientNetTime　model　is　verified　and　compared　against　the　state-of-the-art　models.　Experimental　results　demonstrate　that　the　EfficientNetTime　model　surpasses　other　gunshot　recognition　methods　in　terms　of　the　accuracy　and　model　size.　IEEE