Wideband spectrum sensing improves the agility of spectrum sensing and spectrum hand-off in cognitive radio systems. In this paper, a distributed wideband spectrum sensing technique over adaptive diffusion networks is proposed. Considering unknown and different channels between the primary and the cognitive users, an averaged received power spectrum across all the cognitive users is estimated by each user using diffusion adaptation techniques. This averaged power spectrum estimate is reliable enough for the users to perform spectrum sensing and make a decision regarding the presence or the absence of the primary user. The simulation results show that the detection performance of the system improves with the number of iterations. Further, a satisfactory detection performance at low SNRs is achieved after a few iterations, which is a desired characteristic for cognitive radio systems. Finally, it is shown that the cooperative technique outperforms the non-cooperative one in terms of estimation accuracy and detection performance.

Scenarios where nodes have limited energy and forward messages of different importances (priorities) are frequent in the context of wireless sensor networks. Tailored to those scenarios, this paper relies on stochastic tools to develop selective message forwarding schemes. The schemes will depend on parameters such as the available battery at the node, the energy cost of retransmitting a message, or the importance of messages. The forwarding schemes are designed for three different cases: 1) when sensors maximize the importance of their own transmitted messages; 2) when sensors maximize the importance of messages that have been successfully retransmitted by at least one of its neighbors; and 3) when sensors maximize the importance of messages that successfully arrive to the sink. More sophisticated schemes will achieve better importance performance, but will also require information from other sensors. The results contribute to identify the variables that, when made available to other nodes, have a greater impact on the overall network performance. Suboptimal schemes that rely on local estimation algorithms and entail reduced computational cost are also designed.

An optimum selective transmission scheme for energy-limited sensor networks, where sensors send or forward messages of different importance (priority), is developed. Considering the energy costs, the available battery, the message importances and their statistical distribution, sensors decide whether to transmit or discard a message so that the importance sum of the effectively transmitted messages is maximized. It turns out that the optimal decision is made comparing the message importance with a time-variant threshold. Moreover, the gain of the selective transmission scheme, compared to a nonselective one, critically depends on the energy expenses, among other factors. Albeit suboptimal, practical schemes that operate under less demanding conditions than those for the optimal one are developed. Effort is placed into three directions: 1) the analysis of the optimal transmission policy for several stationary importance distributions; 2) the design of a transmission policy with invariant threshold that entails asymptotic optimality; and 3) the design of an adaptive algorithm that estimates the importance distribution from the actual received (or sensed) messages. Numerical results corroborating our theoretical claims and quantifying the gains of implementing the selective scheme close this paper.

Energy is a valuable resource in wireless sensor networks since it constitutes a limiting factor for the network lifetime. In order to make an efficient use of its own energy resources, each node in the network should be aware of the energy resources at other nodes, which can be relevant to the success of their routing decisions. The proposal of this paper is twofold: (i) to design a routing algorithm based on learning patterns using geographic information and (ii) to focus on the cut down in energy consumption. We show that by exploiting local information from the signals detected at each node, sensor nodes can learn to route messages in order to improve the communication performance of the overall network and minimize the need of coordination or signalling protocols among nodes. Moreover, if messages are prioritized by some importance parameter, the overall importance of the successfully transmitted messages can be drastically improved. Experimental results highlight that our algorithm achieves a good performance in terms of successful delivery rate and maximizes the importance of the received messages.