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

Optimal Compression and Transmission Rate Control for Node-Lifetime Maximization

We consider a system that is composed of an energy constrained sensor node and a sink node, and devise optimal data compression and transmission policies with an objective to prolong the lifetime of the sensor node. While applying compression before transmission reduces the energy consumption of transmitting the sensed data, blindly applying too much compression may even exceed the cost of transmitting raw data, thereby losing its purpose. Hence, it is important to investigate the trade-off between data compression and transmission energy costs. In this paper, we study the joint optimal compression-transmission design in three scenarios which differ in terms of the available channel information at the sensor node, and cover a wide range of practical situations. We formulate and solve joint optimization problems aiming to maximize the lifetime of the sensor node whilst satisfying specific delay and bit error rate (BER) constraints. Our results show that a jointly optimized compression-transmission policy achieves significantly longer lifetime (90% to 2000%) as compared to optimizing transmission only without compression. Importantly, this performance advantage is most profound when the delay constraint is stringent, which demonstrates its suitability for low latency communication in future wireless networks.Comment: accepted for publication in IEEE Transactions on Wireless Communicaiton

A Low-Power CoAP for Contiki

Internet of Things devices will by and large be battery-operated, but existing application protocols have typically not been designed with power-efficiency in mind. In low-power wireless systems, power-efficiency is determined by the ability to maintain a low radio duty cycle: keeping the radio off as much as possible. We present an implementation of the IETF Constrained Application Protocol (CoAP) for the Contiki operating system that leverages the ContikiMAC low-power duty cycling mechanism to provide power efficiency. We experimentally evaluate our low-power CoAP, demonstrating that an existing application layer protocol can be made power-efficient through a generic radio duty cycling mechanism. To the best of our knowledge, our CoAP implementation is the first to provide power-efficient operation through radio duty cycling. Our results question the need for specialized low-power mechanisms at the application layer, instead providing low-power operation only at the radio duty cycling layer

Greening and Optimizing Energy Consumption of Sensor Nodes in the Internet of Things through Energy Harvesting: Challenges and Approaches

This paper presents a survey of current energy efficient technologies that could drive the IoT revolution while examining critical areas for energy improvements in IoT sensor nodes. The paper reviews improvements in emerging energy techniques which promise to revolutionize the IoT landscape. Moreover, the current work also studies the sources of energy consumption by the IoT sensor nodes in a network and the metrics adopted by various researchers in optimizing the energy consumption of these nodes. Increasingly, researchers are exploring better ways of sourcing sufficient energy along with optimizing the energy consumption of IoT sensor nodes and making these energy sources green. Energy harvesting is the basis of this new energy source. The harvested energy could serve both as the principal and alternative energy source of power and thus increase the energy constancy of the IoT systems by providing a green, sufficient and optimal power source among IoT devices. Communication of IoT nodes in a heterogeneous IoT network consumes a lot of energy and the energy level in the nodes depletes with time. There is the need to optimize the energy consumption of such nodes and the current study discusses this as well

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig