Throughput maximization is one of the core challenges in cognitive radio ad hoc networks (CRANs), where local spectrum resources are changing over time and locations. This paper proposes a spectrum and energy aware routing (SER) protocol for CRANs, which involves spectrum aware, and energy-efficient route selection, and channel-timeslot assignment. A good routing protocol should be aware of the interference as well as the end-to-end delay. The proposed joint spectrum and energy aware routing with channel-timeslot assignment can balance the energy consumption, eliminates contention between users, and decompose contending traffics over different channels and timeslots. As a result, the proposed scheme leads to significant increases in network throughput and decreases the end-to-end delay. The simulation results show the effectiveness of our proposed approach with good generalization ability.

Classification is one of the data mining problems receiving enormous attention in the database community. Although artificial neural networks (ANNs) have been successfully applied in a wide range of machine learning applications, they are however often regarded as black boxes, i.e., their predictions cannot be explained. To enhance the explanation of ANNs, a novel algorithm to extract symbolic rules from ANNs has been proposed in this paper. ANN methods have not been effectively utilized for data mining tasks because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by human experts. With the proposed approach, concise symbolic rules with high accuracy, that are easily explainable, can be extracted from the trained ANNs. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and the accuracy. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of benchmark data mining classification problems.

Routing is one of the most important issues in multihop ad hoc networks. In the routing for mobile cognitive radio (CR) networks, the constraints on residual energy of each user and the requirements for primary user protection should be additionally taken into account. This study proposes a spectrum and energy-aware routing (SER) protocol for CR ad hoc networks, which is based on the dynamic source routing. To establish routes on-demand with reserved bandwidth, the protocol involves route selection and channel–timeslot allocation jointly. The proposed SER protocol can balance energy consumption, reduce routing overhead, eliminate contention between nodes and decompose traffics over different channels/timeslots. Extensive simulation shows that the proposed protocol achieves good performance in system throughput, network lifetime and mean message delay.

In this paper, we propose a dynamic TDMA slot reservation (DTSR) protocol for cognitive radio ad hoc networks. Quality of Service (QoS) guarantee plays a critically important role in such networks. We consider the problem of providing QoS guarantee to users as well as to maintain the most efficient use of scarce bandwidth resources. A dynamic frame length expansion and shrinking scheme that controls the excessive increase of unassigned slots has been proposed. This method efficiently utilizes the channel bandwidth by assigning unused slots to new neighboring nodes and increasing the frame length when the number of slots in the frame is insufficient to support the neighboring nodes. It also shrinks the frame length in an effective way. Our proposed scheme, which provides both QoS guarantee and efficient resource utilization, be employed to optimize the channel spatial reuse and maximize the system throughput. Extensive simulation results show that the proposed mechanism achieves significant performance improvement in multichannel cognitive radio ad hoc networks.

Wireless sensor networks (WSNs), part of the fast emerging Internet of Things will be playing vital role in transforming our lives. Potentially huge number of these sensors will soon be deployed to perform simple to complex tasks such as automatically controlling heat and light in smart homes to guiding autonomous vehicles and missiles. A big challenge in WSN is providing energy. Battery management and maintenance of these sensors can be a prohibitively expensive exercise because of their abundance. Sensors' performance will deteriorate with the remaining energy level and dead sensor nodes will affect the performance of an entire network, especially in multi-hop networks. Improper battery disposal will also cause environmental pollution and health hazardous in the long run. The energy issue can be alleviated by energy harvesting. The ambience has energy in the forms of light, heat, mechanical vibrations and electromagnetic radiations. This energy has to be collected with appropriate transducers, stored reliably and used for wireless data transmission which is typically happens as bursts. The sporadic and unreliable nature of the energy harvesting process as well as characteristics of electronic elements has to be appropriately modeled to design viable energy harvesting WSN.