**Abstract**

Wireless sensor networks (WSNs) consist of large number of sensor nodes densely deployed in monitoring area with sensing, wireless communications and computing capabilities. In recent times, wireless sensor networks have used the concept of mobile agent for reducing energy consumption and for effective data collection. The fundamental functionality of WSN is to collect and return data from the sensor nodes. Data aggregation's main goal is to gather and aggregate data in an efficient manner. In data gathering, finding the optimal itinerary planning for the mobile agent is an important step. However, a single mobile agent itinerary planning approach suffers from two drawbacks, task delay and large size of the mobile agent as the scale of the network is expanded. To overcome these drawbacks, this research work proposes: (i) an efficient data aggregation scheme in wireless sensor network that uses multiple mobile agents for aggregating data and transferring it to the sink based on itinerary planning and (ii) an attack detection using TS fuzzy model on multi-mobile agent-based data aggregation scheme is shortly named as MDTSF model.

**Keywords:** wireless sensor network, data aggregation, TS fuzzy, genetic algorithm and itinerary planning, firefly algorithm, minimum spanning tree (MST)

#### **1. Introduction**

Data aggregation signifies inspiring and well-researched topics in the wireless sensor network (WSN) [1–5] writings. The energy restrictions of nodes in a sensor network call for fuel-saving data aggregation approaches and encompass the nexus lifecycle. In addition, mobile agents (MAs) [3, 6] are projected to better the execution of data assemblage in WSNs. In these methodologies, schedules ensuing that traveling agents mainly control the total accomplishment of the collection of the data management. Gathering data effectively has always been of primary importance in a wireless sensor network. The mobile agent paradigm [7, 8] has made it possible to collect and aggregate data in a manner that is proper for real-time applications. Along this line, a number of heuristics have been scheduled to achieve effective itinerary planning for MAs [9].

#### **2. Overview**

Inside the complexity of the sensor, sensor nodes perhaps induce dispensable data; similar packets from various junctions can be accumulated to such an extent that the several broadcasts could be condensed. Data aggregation [1] is the mixture of statistics from different origins by using utilities for example repression (eliminating copies), lowest level, highest level, and median. A few of these tasks can be achieved by the aggregator sensor node, by allowing sensory points to supervise data network depletion. Knowing that calculation would be less power absorbing than transmission, considerable reduction in energy can be achieved by data aggregation [10]. The potency of data aggregation can be deduced using many metrics [11].
