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

Participatory sensing as an enabler for self-organisation in future cellular networks

In this short review paper we summarise the emerging challenges in the field of participatory sensing for the self-organisation of the next generation of wireless cellular networks. We identify the potential of participatory sensing in enabling the self-organisation, deployment optimisation and radio resource management of wireless cellular networks. We also highlight how this approach can meet the future goals for the next generation of cellular system in terms of infrastructure sharing, management of multiple radio access techniques, flexible usage of spectrum and efficient management of very small data cells

A performance simulation tool for the analysis of data gathering in both terrestrial and underwater sensor networks

Wireless sensor networks (WSNs) have greatly contributed to human-associated technologies. The deployment of WSNs has transcended several paradigms. Two of the most significant features of WSNs are the intensity of deployment and the criticalness of the applications that they govern. The tradeoff between volume and cost requires justified investments for evaluating the multitudes of hardware and complementary software options. In underwater sensor networks (USNs), testing any technique is not only costly but also difficult in terms of full deployment. Therefore, evaluation prior to the actual procurement and setup of a WSN and USN is an extremely important step. The spectrum of performance analysis tools encompassing the test-bed, analysis, and simulation has been able to provide the prerequisites that these evaluations require. Simulations have proven to be an extensively used tool for analysis in the computer network field. A number of simulation tools have been developed for wired/wireless radio networks. However, each simulation tool has several restrictions when extended to the analysis of WSNs. These restrictions are largely attributed to the unique nature of each WSN within a designated area of research. In addition, these tools cannot be used for underwater environments with an acoustic communication medium, because there is a wide range of differences between radio and acoustic communications. The primary purpose of this paper is to present, propose, and develop a discrete event simulation designed specifically for mobile data gathering in WSNs. In addition, this simulator has the ability to simulate 2-D USNs. This simulator has been tailored to cater to both mobile and static data gathering techniques for both topologies, which are either dense or light. The results obtained using this simulator have shown an evolving efficient simulator for both WSNs and USNs. The developed simulator has been extensively tested in terms of its validity and scope of governance

Node placement in Wireless Mesh Networks: a comparison study of WMN-SA and WMN-PSO simulation systems

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.With the fast development of wireless technologies, Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure due to their low cost and increased high speed wireless Internet connectivity. In our previous work, we implemented a simulation system based on Simulated Annealing (SA) for solving node placement problem in wireless mesh networks, called WMN-SA. Also, we implemented a Particle Swarm Optimization (PSO) based simulation system, called WMN-PSO. In this paper, we compare two systems considering calculation time. From the simulation results, when the area size is 32 × 32 and 64 × 64, WMN-SA is better than WMN-PSO. When the area size is 128 × 128, WMN-SA performs better than WMN-PSO. However, WMN-SA needs more calculation time than WMN-PSO.Peer ReviewedPostprint (author's final draft