Energy-aware task allocation for energy harvesting sensor networks

10.1186/s13638-015-0490-3Eurasip Journal on Wireless Communications and Networking201611-1

From Task Graphs to Concrete Actions: A New Task Mapping Algorithm for the Future Internet of Things

International audienceTask mapping, which basically consists of mapping a set of tasks onto a set of nodes, is a well-known problem in distributed computing research. As a particular case of distributed systems, the Internet of Things (IoT) poses a set of renewed challenges, because of its scale, heterogeneity and properties traditionally associated with wireless sensor networks (WSN), shared sensing, continous processing and real time computing. To handle IoT features, we present a formalization of the task mapping problem that captures the varying consumption of resources and various constraints (location, capabilities, QoS) in order to compute a mapping that guarantees the lifetime of the concurrent tasks inside the network and the fair allocation of tasks among the nodes. It results in a binary programming problem for which we provide an efficient heuristic that allows its resolution in polynomial time. Our experiments show that our heuristic: (i) gives solutions that are close to optimal and (ii) can be implemented on reasonably powerful Things and performed directly within the network, without requiring any centralized infrastructure

Towards Energy - Efficient Qos-Aware Online Stream Data Processing for Internet of Things

Online data stream processing in Internet of Things (IoT) systems is an emerging paradigm that allows users to use resource-constrained IoT devices with the back- end of resourceful machines to process the data collected from the physical world in a real-time manner. The huge amount of generated sensor data can produce value- added information with different purposes for several applications. Techniques to pro- mote knowledge discovery from the raw data allow fully exploiting the potential usage of wide spread sensors in the IoT. In this context, using the energy of the resource- constrained IoT devices in an efficient way is a major concern. However, the appli- cation of QoS requirements should not be ignored to achieve the purpose of energy saving at any cost. In this thesis, we propose a framework that combines online stream data processing with adaptive system control to address both needs. The online algo- rithms are based on statistical methods to meet the needs of stream data processing. The result of the algorithms are then used to dynamically control the system behaviour to meet the needs of energy-saving. Simulation results show the effectiveness of our proposed framework