In　a　wireless　sensor　network　(WSN),　the　usage　of　resources　is　usually　highly　related　to　the　execution　of　tasks　which　consume　a　certain　amount　of　computing　and　communication　bandwidth.　Parallel　processing　among　sensors　is　a　promising　solution　to　provide　the　demanded　computation　capacity　in　WSNs.　Task　allocation　and　scheduling　is　a　typical　problem　in　the　area　of　high　performance　computing.　Although　task　allocation　and　scheduling　in　wired　processor　networks　has　been　well　studied　in　the　past,　their　counterparts　for　WSNs　remain　largely　unexplored.　Existing　traditional　high　performance　computing　solutions　cannot　be　directly　implemented　in　WSNs　due　to　the　limitations　of　WSNs　such　as　limited　resource　availability　and　the　shared　communication　medium.　In　this　paper,　a　self-adapted　task　scheduling　strategy　for　WSNs　is　presented.　First,　a　multi-agent-based　architecture　for　WSNs　is　proposed　and　a　mathematical　model　of　dynamic　alliance　is　constructed　for　the　task　allocation　problem.　Then　an　effective　discrete　particle　swarm　optimization　(PSO)　algorithm　for　the　dynamic　alliance　(DPSO-DA)　with　a　well-designed　particle　position　code　and　fitness　function　is　proposed.　A　mutation　operator　which　can　effectively　improve　the　algorithm＇s　ability　of　global　search　and　population　diversity　is　also　introduced　in　this　algorithm.　Finally,　the　simulation　results　show　that　the　proposed　solution　can　achieve　significant　better　performance　than　other　algorithms.