Performance evaluation of wireless sensor networks for mobile event and mobile sink

Extending lifetime and energy efficiency are important objectives and challenges in-Wireless Sensor Networks (WSNs). In large scale WSNs, when the nodes are near to the sink they consume much more energy than the nodes far from the sink. In our previous work, we considered that the sink node was stationary and only event node was moving in the observation field. In this work, we consider both cases when the sink node and event node are moving. For the simulations, we use TwoRayGround and Shadowing radio models, lattice topology and AODV protocol. We compare the simulation results for the cases when the sink node and event node are mobile and stationary. The simulation results have shown that the goodput of TwoRayGround is better than Shadowing in case of mobile event, but the depletion of Shadowing is better than TwoRayGround in case of mobile event. The goodput in case of mobile sink is better than stationary sink when the transmission rate is lower than 10pps. For TwoRayGround radio model, the depletion in case of mobile sink is better than stationary sink when the number of nodes is increasedPeer ReviewedPostprint (published version

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

An evolutionary behavioral model for decision making

For autonomous agents the problem of deciding what to do next becomes increasingly complex when acting in unpredictable and dynamic environments pursuing multiple and possibly conflicting goals. One of the most relevant behavior-based model that tries to deal with this problem is the one proposed by Maes, the Bbehavior Network model. This model proposes a set of behaviors as purposive perception-action units which are linked in a nonhierarchical network, and whose behavior selection process is orchestrated by spreading activation dynamics. In spite of being an adaptive model (in the sense of self-regulating its own behavior selection process), and despite the fact that several extensions have been proposed in order to improve the original model adaptability, there is not a robust model yet that can self-modify adaptively both the topological structure and the functional purpose\ud of the network as a result of the interaction between the agent and its environment. Thus, this work proffers an innovative hybrid model driven by gene expression programming, which makes two main contributions: (1) given an initial set of meaningless and unconnected units, the evolutionary mechanism is able to build well-defined and robust behavior networks which are adapted and specialized to concrete internal agent's needs and goals; and (2)\ud the same evolutionary mechanism is able to assemble quite\ud complex structures such as deliberative plans (which operate in the long-term) and problem-solving strategies