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With the advent of ubiquitous computing, a user is surrounded by a variety of devices including tiny sensor nodes, handheld mobile devices and powerful computers as well as diverse communication networks. In this networked society, the role of a human being is evolving from the data consumer to the data producer. In these changing circumstances, pipelined processing finds applications where the data obtained from the human producer needs to be processed and interpreted in real-time. For example, in an M- Health system, the vital signs acquired from the patient are processed in the pipelined fashion. This paper proposes a genetic algorithm (GA) based approach for the optimal assignment of pipelined processing tasks onto a chain of networked devices that minimizes total end-to-end processing delay considering knowledge about the communication and computation resources as the context information. Although some existing graph-based algorithms can solve this problem in polynomial time, we expect that GA can take less computational time and requires less memory while providing a reasonably good assignment. We compare the performance of GA approach with the graph-based approaches. It is observed that when the number of devices and the number of processing tasks are large, the GA approach performs better in terms of the satisfactory quality of the obtained sub-optimal solution considering the advantage of reduced computational time.