Power-aware routing in mobile ad hoc networks
MobiCom '98 Proceedings of the 4th annual ACM/IEEE international conference on Mobile computing and networking
Efficient algorithms for maximum lifetime data gathering and aggregation in wireless sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
An adaptive energy-efficient MAC protocol for wireless sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Fundamental scaling laws for energy-efficient storage and querying in wireless sensor networks
Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing
Maximizing lifetime for data aggregation in wireless sensor networks
Mobile Networks and Applications
Real-time data aggregation in contention-based wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
ISWPC'10 Proceedings of the 5th IEEE international conference on Wireless pervasive computing
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Sensor networks are expected to be used for spatial cognition in harsh environments. When an event happens, several sensors will detect it and send their reports to a sink. However, these data are neither integrated nor reliable. Therefore, it is reasonable to make use of data fusion on intermediate nodes to achieve deep knowledge about an event and to reduce the total traffic on a sensor network at the same time. In order to prolong the lifetimes of sensor networks with data fusion and QoS requirements, we propose a genetic algorithm to balance the loads of energy consumptions of all nodes. It answers how nodes should distribute their data flow on outgoing links. We call this traffic planning. Additionally, in a sensor network with data fusion, a node should decide the order in which it sends packets to the neighbors. This affects the time that an intermediate node should wait for all incoming data arriving before it can apply data fusion. We call this traffic scheduling problem. Different traffic schedulings result in different delays. We propose a heuristic algorithm to reduce this delay. Furthermore, sensor networks are generally developed for special applications. If different sensor networks deployed in a same region can cooperate with each other in data transmission, their lifetimes can be improved remarkably. We call this Multi-SensorNets. In this paper, we take the problems of lifetime maximization and sleeping scheduling into account synchronously on Multi-SensorNets and show the efficiency of our approaches by experiments.