An Energy-Efficient Multiobjective Scheduling Model for Monitoring in Internet of Things

Abstract

To ensure robustness in wireless networks, monitoring the network state, performance and functioning of the nodes and links is crucial, especially for critical applications. This paper targets Internet of Things (IoT) networks. In the IoT, devices (things) are vulnerable due to security risks from the Internet. Moreover, they are resource-constrained and connected via lossy links. This paper addresses the optimized scheduling of the monitoring role between the embedded devices in IoT networks. The objective is to minimize energy consumption and communication overhead of monitoring, for each node. Several subsets of the potential monitoring nodes are generated by solving a minimal vertex cover (VC) problem with constraint generation. Assuming periodical functioning, VCs are optimally assigned to time periods in order to distribute the monitoring role throughout the entire network. The assignment of VCs to periods is modeled as a multiobjective generalized assignment problem. To further optimize the energy consumption of the monitors, they are sequenced across time periods to minimize the state transitions of nodes. This part of the problem is modeled as a traveling salesman path problem. The proposed model is tested on randomly generated instances and the experimental results illustrate its effectiveness to optimize the scheduled monitoring for fault tolerance in IoT networks. Index Terms-Energy-efficient monitoring, generalized assignment problem (GAP), Internet of Things (IoT), robustness, scheduling, traveling salesman path (TSP) problem, vertex cover (VC). I. INTRODUCTION AND MOTIVATIONT HE Internet of Things (IoT) is a persistently growing network that seamlessly interconnects a tremendous number of heterogeneous, smart devices (things) with the Internet. The connection does not require human-to-human Manuscript

Performance Analysis of Cognitive Radio Based Internet-Of-Things Network for Energy Efficiency and Spectrum Utilization

“…The best quality channel in terms T of stability and reliability was assigned to the sensor nodes by proposing a channel management mechanism to achieve reliable intra-cluster reporting. Highly effective results were achieved which showed that this approach provided highly spectrum efficient mechanism and was also better that previously existing approaches Basma Mostafa, et.al (2018) proposed an III-phase mathematical model for handling the optimized scheduling within IoT networks [10]. The Vertex Cover Problem was solved iteratively in the initial phase to generate the different subsets of nodes which cover the complete graph.…”

Optimizing the Lifetime of IoT-Based Star and Mesh Networks

“…Our work is different from [30] in that our parameter tuning is at the network and application layers, while their parameter tuning is at the physical and MAC layers. In [31], the authors propose an energy-efficient multi-objective scheduling model for monitoring-based IoT networks. Their objective is to minimize energy consumption and communication overhead of monitoring for each node while considering link faults due to energy limitations.…”

“…Their work however only considers IoT networks operating on a generated destination oriented directed acyclic graph (DODAG). Also, unlike our work which considers faults due to security, hardware, and energy failures, their work [31] only considers faults due to energy limitations. Rango et al [32] analyze energy-aware communication between smart IoT monitoring devices.…”

“…j : This parameter reflects the security failure probability of node j at location x due to capture attacks (theft or device tampering so a device is compromised and does not perform the intended functions). It can be measured by the environmental cloud by leveraging Geographic Information System (GIS)-based crime mapping tools and applications [31], [32] which provide the number of incidents logged in a location x over a time period with which the security failure rate at a location can be derived. With the information of security failure rate for node j at location x known, the environmental cloud can parameterize comp x j as the insecurity of the sensor.…”

CMTN-SP: A Novel Coverage-Control Algorithm for Moving-Target Nodes Based on Sensing Probability Model in Sensor Networks

“…The sensor network is a large-scale and self-organized sensing network which could achieve the information world integration and the physical world integration [1,2,3,4,5]. The deployment of this network is achieved by densely and randomly deploying the nodes which are in the monitoring area, while the sensor nodes behavior is mainly characterized by: a certain degree of computation, communication, storage and control ability, which could accomplish the collection, communication, computation, and storage of the information from the physical world [6,7,8,9]. The high-speed visual sensors are adopted by this work.…”