Modelling and QoS Implementation of Wireless Sensor Networks based on the Ant Colony Optimization Approach

Abstract

A new form of wireless sensor networks is emerging as an important component of the Internet of Things (IoT) where camera devices are interconnected and endowed with an IP address to form visual sensor networks. The applications of these networks span from smart parking systems in smart cities, video surveillance for security systems to healthcare monitoring and many others which are emerging from niche areas. The management of such sensor networks will require meeting a higher quality of service (QoS) constraints than demanded from traditional sensor networks. While many works have focused only on energy efficiency as a way of providing QoS in sensor networks, we consider a novel modelling approach where local optimizations implemented on the sensor nodes are translated into pheromone distribution used in ant colony optimization for path computation. We propose a routing protocol called the multipath ant colony optimization (MACO) that finds QoS-aware routing paths for the sensor readings from source nodes to the sink by relying on four local parameters: the link cost, the remaining energy of neighboring nodes, sensor nodes location information and the amount of data a neighbor node is currently processing. Finally, we propose an architecture for integrating sensor data with the cloud computing. Simulation results reveal the relative efficiency of the newly proposed approach compared to selected related routing protocols in terms of several QoS metrics. These include the network energy efficiency, delay and throughput.

DACASN: Dynamic ant routing‐based channel accessing cognitive sensor network in Internet of Things

“…22 The management of such sensor networks would require meeting higher QoS constraints than that of traditional sensor networks. 23 While many studies have focused only on energy efficiency 24 as a way of providing QoS in sensor networks 25 integrate the spectrum sensing at the PHY layer and packet scheduling at the MAC layer. 26 The drive of network intellect from controlling resources to understanding user needs would help "flatten" the network by moving network intellect further out towards the boundaries of the network.…”