Efficient Cluster-Based Sleep Scheduling for M2M Communication Network

Abstract

Machine-to-machine (M2M) communication networks comprise a large number of machine-type communication (MTC) devices such as sensors, radio frequency identification readers, and smart meters. Thus, M2M networks are becoming popular in real-time monitoring, surveillance, and security applications. Scheduling the active and idle states of MTC devices is significantly important to achieve a longer network lifetime and reduce collision during data transmission. Existing node sleep scheduling schemes are mainly designed to reduce the energy consumption of MTC devices. These schemes do not support mobility of MTC devices and thus cannot be used in mobility-centric M2M applications. Thus, we propose a cluster-based energy-efficient, mobility-centric node scheduling scheme for M2M (CENM) communication networks. The proposed CENM scheduling scheme provides (i) network coverage by keeping a minimum number of MTC devices in active state and (ii) fault tolerance by selecting alternative cluster heads and member nodes. Simulation results show that the CENM scheduling scheme is more reliable than the existing CCNS scheduling scheme since the first node in CENM scheme fails much later than that in CCNS scheduling scheme. Simulation results also show that the CENM scheduling scheme outperforms the CCNS, LEACH-M and LEACH-ME scheduling schemes in terms of network energy consumptions, network lifetime, and total message transmissions.

Dynamic Data Aggregation Approach for Sensor-Based Big Data

“…Thus, this paper introduces an energy efficient application dependent data aggregation approach for sensorbased big data frameworks. Sensors are programmed to have a data type field in their packets so that other sensors or devices that receive the data packet can identify the type of applications and perform data aggregation based on the data type [21]. This field also helps to store data at the appropriate locations in big data server for further processing and use.…”

Temporal Data-Driven Sleep Scheduling and Spatial Data-Driven Anomaly Detection for Clustered Wireless Sensor Networks

“…They focused on balanced energy consumption, which was realized by considering the residual energy and the distance among nodes. Aimed at keeping high network coverage through a minimum number of sensor devices in active state, Al-Kahtani et al [5] presented an efficient cluster-based sleep scheduling. In their work, the sensing range of the node is larger than the distance between nodes.…”

“…Aimed at reducing the energy consumption caused by data sensing, sparse sensing based on the spatial–temporal correlation can reduce the amount of collected samples [4]. To reduce the energy consumption caused by node operation, the spatial–temporal correlation can be used to realize the sleep scheduling of nodes [5]. In addition, the sleep scheduling can also reduce the amount of collected samples and transmitted data, so that it is a significant method for reducing the energy consumption of sensor nodes.…”