Edge　computing　offloading　involves　moving　computational　tasks　from　traditional　cloud　computing　data　centers　to　edge　servers　or　terminal　devices　closer　to　data　sources,　or　idle　resource　devices.　This　aims　to　reduce　task　response　times　and　enhance　service　efficiency.　However,　the　dynamic　and　unreliable　nature　of　edge　computing　environments　can　lead　to　malicious　attacks　on　resource　devices,　potentially　compromising　system　trustworthiness.　Current　research　primarily　focuses　on　performance　optimization　and　latency　reduction,　often　overlooking　service　trustworthiness.　Therefore,　this　paper　addresses　resource　device　trustworthiness　in　edge　computing,　proposing　an　efficient　multi-source　trust　aggregation　scheme　to　ensure　the　selection　of　trustworthy　service　providers　for　edge　computing　offloading.　It　establishes　trust　relationships,　combines　direct　and　indirect　trust,　and　introduces　time　decay　and　energy　factors　for　trust　evaluation.　Furthermore,　the　paper　integrates　trust　measurement　with　collaborative　filtering　and　presents　an　adaptive　aggregation　algorithm　for　calculating　and　updating　global　trustworthiness.　Through　simulation　experiments,　it　validates　the　effectiveness　of　the　proposed　scheme　in　the　selection　of　service　providers　for　edge　computing　offloading.　The　results　demonstrate　that　this　approach　effectively　filters　unreliable　recommendations,　reduces　interference　from　malicious　nodes,　and　enhances　the　trustworthiness　and　stability　of　edge　computing　offloading　services.　©　2024,　The　Author(s),　under　exclusive　licence　to　Springer-Verlag　GmbH　Austria,　part　of　Springer　Nature.