Energy-collision-aware Minimum Latency Aggregation Scheduling for Energy-harvesting Sensor Networks

The emerging energy-harvesting technology enables charging sensor batteries with renewable energy sources, which has been effectively integrated into Wireless Sensor Networks (EH-WSNs). Due to the limited energy-harvesting capacities of tiny sensors, the captured energy remains scarce and differs greatly among nodes, which makes the data aggregation scheduling problem more challenging than that in energy-abundant WSNs. In this article, we investigate the Minimum Latency Aggregation Scheduling (MLAS) problem in EH-WSNs. First, we identify a new kind of collision in EH-WSNs, named as energy-collision, and design several special structures to avoid it during data aggregation. To reduce the latency, we try to choose the parent adaptively according to nodes’ transmission tasks and energy-harvesting ability, under the consideration of collisions avoidance. By considering transmitting time, residual energy, and energy-collision, three scheduling algorithms are proposed under protocol interference model. Under physical interference model, several approximate algorithms are also designed by taking account of the interference from the nodes several hops away. Finally, the theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency.

Optimizing the Capacity of Cognitive Radio Networks with Power Control and Variable Spectrum Allocation

Cognitive Radio Networks (CRN) were introduced as a means to more efficiently reuse the licensed radio frequency spectrum. One of their salient features is the ability of unlicensed nodes to dynamically adapt their radio parameters according to their needs. This paper investigates the resource allocation problem in CRN by jointly considering power control and bandwidth for a set of secondary users (SU) transmitting simultaneously with a set of licensed users (PU), which transmissions must remain unaltered. The proposed allocation scheme is based on a Genetic Algorithm (GA) where the chromosome's genes represent the reconfigurable interface radio parameters, by defining genetic operations the GA is empowered to find a set of radio parameters that maximize the overall network capacity and under the physical interference model enforced to the transmissions of both PU’s and SU’s. The numerical results illustrate the prominent effect of adjusting jointly multiple radio parameters on the network capacity.

Minimum-latency data aggregation scheduling based on multi-path routing structures under physical interference model

Minimum-Latency Aggregation Scheduling is a significant problem in wireless sensor networks. The key challenge is to find an effective solution to aggregate data from all sensors to the sink with minimum aggregation latency. In this article, we propose a novel data aggregation scheduling algorithm under the physical interference model. First, the algorithm partitions the network into square cells according to the communication range of a sensor. Second, a node is selected randomly as the aggregated node to receive the data from the other nodes in the same cell. Finally, a data aggregation tree, which consists of multiple disjoint paths, is constructed to aggregate data from all aggregated nodes to the sink. We empirically proved that the delay of the aggregation schedule generated by our algorithm is ( K+1)2Δ− K−1+2λ time-slots at most, where K is a constant depending on the sensors transmitting power, the signal-to-interference-plus-noise-ratio threshold, and the path-loss exponent; [Formula: see text] represents the maximal number of nodes in a cell; and [Formula: see text] denotes the number of cells at a row/column in a square network area. Simulation results also show that our algorithm achieves lower average latency than the previous works.