Multiagent System In-network Processing In Wireless Sensor Network

Abstract

Wireless Sensor Network (WSN) consists of embedded devices attaching sensor and microelectronic technology, computational capability, and distributed processing. Due to computing capability, it is interesting to implement in-network processing in WSN. It means that data preprocessing is conducted in network to reduce amount of data and processing time in server. One of in-network processing approach is multiagent-based signal processing. Here, Agents deployed in sensor nodes, including mobile agents, make collaboration for data aggregation and preprocessing. This paper describes our proposed method how multiagent system is implemented in real WSN. The method lists the process how global network information is identified by agents in sensor nodes, mobile agent migration plan is conducted by the intelligent agent in head clusters, and the mobile agent migrates hop by hop and performs collaboration with sensor node agents for data preprocessing. We propose vibration of a concrete beam as the case study. Our simulation and experimental results show that the proposed multiagent system works properly.

Proximate node aware optimal and secure data aggregation in wireless sensor network based IoT environment

“…Further, when 𝑘 is greater than or equal to 1, then 𝑧̂𝑘(𝑚) is updated and it will be different from the initial state since each update comprises the averaging process from the information through the proximate nodes. Thus, for all 𝑘 ∈ 𝑃 𝑘 , the information set for the available node 𝑖 at given iteration 𝑚 is given as (7).…”

“…An increase in the value of the correlation between the information accumulated by multiple sensors increases the efficiency of the DA. DA can successfully enhance the data accumulation's energy efficiency where the aggregation of the sensed information is done by the relaying nodes [7]. DA has a plethora of applications because of its dominance inefficient energy usage.…”

Intelligent Sensing in Multiagent-Based Wireless Sensor Network for Bridge Condition Monitoring System

“…The scopes of this work specifically include the multiagent system design for in-network processing implementation in bridge condition monitoring system that recontextualizes our previous works [31] and [32], the development of effective sensing method that employs agents in two WIM nodes in such a way to control when sensing and data processing should be conducted, and the two-player game and reinforcement learning [33] recontextualization in two WIMs interaction model.…”

Novel wireless charging algorithms to charge mobile wireless sensor network by using reinforcement learning

“…Yahyaouy et al [26] presented multi-agent systems useful in decision making systems. Learning algorithms mainly derived from reinforcement learning with various sets of interaction and trial with the unknown environment, the learning agent uses the reward to develop the policy which later on optimizes the policy plan along with path planning of sensor nodes and mobile charger [27][28][29]. The sensor nodes and mobile chargers can become learning agents to plan for charging path in an unknown environment [30][31][32].…”