Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Deep Learning Meets Cognitive Radio: Predicting Future Steps

Learning the channel occupancy patterns to reuse the underutilised spectrum frequencies without interfering with the incumbent is a promising approach to overcome the spectrum limitations. In this work we proposed a Deep Learning (DL) approach to learn the channel occupancy model and predict its availability in the next time slots. Our results show that the proposed DL approach outperforms existing works by 5%. We also show that our proposed DL approach predicts the availability of channels accurately for more than one time slot

Secure Data Aggregation in Vehicular-Adhoc Networks: A Survey

AbstractVehicular ad hoc networks (VANETs) are an upcoming technology that is gaining momentum in recent years. That may be the reason that the network attracts more and more attention from both industry and academia. Due to the limited bandwidth of wireless communication medium, scalability is a major problem. Data aggregation is a solution to this. The goal of data aggregation is to combine the messages and disseminate this in larger region. While doing aggregation integrity of the information can not be easily verified and attacks may be possible. Hence aggregation must be secure. Although there are several surveys covering VANETs, they do not concentrate on security issues specifically on data aggregation. In this paper, we discuss and analyse various data aggregation techniques and their solutions