Cognitive radio technology is a promising solution to the exponential growth in bandwidth demand sustained by increasing number of ubiquitous connected devices. The allocated spectrum is opened to the secondary users conditioned on limited interference on the primary owner of the band. A major bottleneck in cognitive radio systems is to find the best available channel quickly from a large accessible set of channels. This work formulates the channel exploration-exploitation dilemma as a multi-arm bandit problem. Existing theoretical solutions to a multi-arm bandit are adapted for cognitive radio and evaluated in an experimental test-bed. It is shown that a Thompson sampling based algorithm efficiently converges to the best channel faster than the existing algorithms and achieves higher asymptotic average throughput. We then propose a multihop extension together with an experimental proof of concept.

In the era of Internet of Things (IoT), the development of Wireless Sensor Networks (WSN) arises different challenges. Two of the main issues are electromagnetic interference and the lifetime of WSN nodes. In this paper, we show and evaluate experimentally the relation between interference and energy consumption, which impacts the network lifetime. We present a platform based on commercially available low-cost hardware in order to evaluate the impact of electromagnetic interference in 2.4 GHz ISM band on energy consumption of WSN. The energy measurements are obtained separately from each electronic component in the node. Interference and energy measurements are conducted in an anechoic chamber and in an office-type lab environment. X-MAC protocol is chosen to manage the Radio Duty Cycle of the nodes and its energy performance is evaluated. The energy consumption transmitter nodes is analyzed particularly in this work. Moreover, this energy consumption has been quantified and differentiated according to the number of (re-)transmissions carried out by the transmitter as well as the number of ACK packets sent by the receiver for a single packet. Finally, we use a model of real battery to calculate the lifetime of the node for operation within different interference level zones. This study lays the basis for further design rules of communication protocols and development of WSNs.

Several wild animal species are endangered by poaching. As a solution, deploying wireless sensors on animals able to send regular messages and also alert messages has been envisaged recently by several authorities and foundations. In that context, this paper proposes WildMAC, a multichannel, multihop wireless communication protocol for these specific wireless sensor networks that have to collect data from unknown large areas with different QoS requirements. WildMAC is a TDMA based MAC protocol that leverages long range communication properties to propose an efficient data collection mean. Its performance evaluation shows it meets QoS requirements.

Abstract-In cellular networks, the outage probability is the probability that the signal-to-interference-plus-noise-ratio (SINR) is less than a given threshold. In this article, firstly, we develop an explicit formula for outage probability for a wireless sensor network taking account of the problem parameters -i.e. the spatial density of nodes in the network, the noise power, the transmission power, the distance between the considered node and the base station, and finally the attenuation coefficient -. Secondly, we model a given node by a Markov chain to study the performance of the MAC layer in the case of a Slotted-ALOHA protocol and Slotted-ALOHA with preliminary channel reservation. Especially in this study, we theoretically estimate the transmission success likelihood after a certain number of attempts that we consider as a metric to evaluate the performance of the considered protocol. Finally we compare the theoretical results with experimental ones achieved on the large scale platform FIT IoT-Lab of Lille. To sum up, this paper responds to the following question: How many channels do we need to a achieve high performance distributed wireless sensor network? The answer is given by the necessary number of channels which maximizes the transmission success likelihood after a certain number of trials.