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![Figure 3. MOMDP scheme [12]](profile/Daniel-Kovacs-3/publication/261154827/figure/fig1/AS:392400057978884@1470566872579/MOMDP-scheme-12_Q320.jpg)
Optimizing overall performance of Wireless Sensor Networks (WSNs) is important due to the limited resources available to nodes. Several aspects of this optimization problem have been studied (e.g. improving Medium Access Control (MAC) protocols, routing, energy management) mostly separately, although there is a strong inter-connection between them....
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Citations
... The application of MDP was surveyed to model various problems in WSN including intrusion detection, sensor coverage, object detection, data exchange, topology formulation and other problems [203]. POMDP have been used for performance optimization [204], data and memory access control [205], and sleep scheduling [206]. IDs were used for lighting control in WSN and provided robustness to sensor uncertainties [207]. ...
Intelligent transport systems, efficient electric grids, and sensor networks for data collection and analysis are some examples of the multi-agent systems (MAS) that cooperate to achieve common goals. Decision making is an integral part of intelligent agents and MAS that will allow such systems to accomplish increasingly complex tasks. In this survey, we investigate state of the art work within the past five years on cooperative MAS decision making models, including Markov decision processes, game theory, swarm intelligence and graph theoretic models. We survey algorithms that result in optimal and sub-optimal policies such as reinforcement learning, dynamic programming, evolutionary computing and neural networks. We also discuss the application of these models to robotics, wireless sensor networks, cognitive radio networks, intelligent transport systems and smart electric grids. In addition, we define key terms in the area and discuss remaining challenges that include incorporating big data advancements to decision making, developing autonomous, scalable and computationally efficient algorithms, tackling more complex tasks and developing standardized evaluation metrics. While recent surveys have been published on this topic, we present a broader discussion of related models and applications.
... Another direction for system configuration is to optimize nodes' run time operations to match the dynamic environment conditions. For example, Kovacs et al. [94] introduced a methodology for dynamically optimizing WSN protocols such as routing, data aggregation, and topology control. Essentially, the considered performance metrics include data gathering delay, energy consumption, and data consistency. ...
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.
This article focused on the energy limit property of Wireless Sensor Network, and proposed a residual energy based algorithm WRECS, which is improved on classical routing algorithm LEACH. The algorithm took the normalized residual energy and the cumulative number of the normal nodes supported by the cluster heads as clusterheads selection parameters. In order to balance the energy consumption of each cluster-head, the algorithm took both the different positions of the base station and the initial energy of the network into consideration, and weighted the two factors to balance the energy consumption between transmission and data fusion. Simulation results show that the algorithm can promote the lifetime of the uneven energy network and does not impair the effects of the LEACH algorithm.
