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

Energy-Aware Constrained Relay Node Deployment for Sustainable Wireless Sensor Networks

© 2016 IEEE. This paper considers the problem of communication coverage for sustainable data forwarding in wireless sensor networks, where an energy-aware deployment model of relay nodes (RNs) is proposed. The model used in this paper considers constrained placement and is different from the existing one-tiered and two-tiered models. It supposes two different types of sensor nodes to be deployed, i) energy rich nodes (ERNs), and ii) energy limited nodes (ELNs). The aim is thus to use only the ERNs for relaying packets, while ELN's use will be limited to sensing and transmitting their own readings. A minimum number of RNs is added if necessary to help ELNs. This intuitively ensures sustainable coverage and prolongs the network lifetime. The problem is reduced to the traditional problem of minimum weighted connected dominating set (MWCDS) in a vertex weighted graph. It is then solved by taking advantage of the simple form of the weight function, both when deriving exact and approximate solutions. Optimal solution is derived using integer linear programming (ILP), and a heuristic is given for the approximate solution. Upper bounds for the approximation of the heuristic (versus the optimal solution) and for its runtime are formally derived. The proposed model and solutions are also evaluated by simulation. The proposed model is compared with the one-tiered and two-tiered models when using similar solution to determine RNs positions, i.e., minimum connected dominating set (MCDS) calculation. Results demonstrate the proposed model considerably improves the network life time compared to the one-tiered model, and this by adding a lower number of RNs compared to the two-tiered model. Further, both the heuristic and the ILP for the MWCDS are evaluated and compared with a state-of-the-art algorithm. The results show the proposed heuristic has runtime close to the ILP while clearly reducing the runtime compared to both ILP and existing heuristics. The results also demonstrate scalability of the proposed solution

Optimal placement of relay nodes over limited positions in wireless sensor networks

This paper tackles the challenge of optimally placing relay nodes (RNs) in wireless sensor networks given a limited set of positions. The proposed solution consists of: 1) the usage of a realistic physical layer model based on a Rayleigh block-fading channel; 2) the calculation of the signal-to-interference-plus-noise ratio (SINR) considering the path loss, fast fading, and interference; and 3) the usage of a weighted communication graph drawn based on outage probabilities determined from the calculated SINR for every communication link. Overall, the proposed solution aims for minimizing the outage probabilities when constructing the routing tree, by adding a minimum number of RNs that guarantee connectivity. In comparison to the state-of-the art solutions, the conducted simulations reveal that the proposed solution exhibits highly encouraging results at a reasonable cost in terms of the number of added RNs. The gain is proved high in terms of extending the network lifetime, reducing the end-to-end- delay, and increasing the goodput

Efficient energy management for the internet of things in smart cities

The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Internet of Things offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoT applications is increased, while IoT devices continue to grow in both numbers and requirements. Therefore, smart city solutions must have the ability to efficiently utilize energy and handle the associated challenges. Energy management is considered as a key paradigm for the realization of complex energy systems in smart cities. In this article, we present a brief overview of energy management and challenges in smart cities. We then provide a unifying framework for energy-efficient optimization and scheduling of IoT-based smart cities. We also discuss the energy harvesting in smart cities, which is a promising solution for extending the lifetime of low-power devices and its related challenges. We detail two case studies. The first one targets energy-efficient scheduling in smart homes, and the second covers wireless power transfer for IoT devices in smart cities. Simulation results for the case studies demonstrate the tremendous impact of energy-efficient scheduling optimization and wireless power transfer on the performance of IoT in smart cities

A survey of network lifetime maximization techniques in wireless sensor networks

Emerging technologies, such as the Internet of things, smart applications, smart grids and machine-to-machine networks stimulate the deployment of autonomous, selfconfiguring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteri