Opportunistic Routing for Indoor Energy Harvesting Wireless Sensor Networks

Internet of Things (IoTs) is envisioned to enable smart spaces such as smart homes, interactive museums or personalized trade in shopping malls. In these smart homes, several sensors and actuators assist in automating tasks of daily life by forming wireless sensor networks (WSNs). Active and Assisted Living (AAL) is one of the important applications of smart homes, wherein activities of elderly persons are mainly monitored and assist them through actuators when required. With a huge number of sensors involved in AAL applications across smart homes, powering the nodes is an important issue. To this end, make use of ambient energy harvesting technologies for enabling perpetual operations of the WSNs. Due to limited energy harvesting opportunities in indoor environments, the energy levels of nodes in the WSN varies over space and time. Therefore, collecting data reliably over such a network is a significant challenge. There are many proposals in this domain, including MAC, routing, etc., in such WSN. Although several routing protocols exist for WSNs and EHWSNs, they do not consider the limited energy availability. Consequently, they do not adapt to the conditions, therefore fail in their objective. We propose a novel routing protocol called Harvesting Energy Aware Routing with adaptive Duty Cycling (HEAR-DC), based on the philosophy of using available energy, and routing the data packets opportunistically. HEAR-DC has several components: (i) transmitter-initiated MAC with opportunistic reception and duplicity avoidance; (ii) an energy harvesting aware gradient; (iii) adaptive duty cycling; and (iv) adaptations to support mobile nodes and sparse data traffic. We also develop a trace-based energy harvesting simulation module for Cooja, which does not exist as yet. With this module, real data traces can be used for evaluating energy-harvesting WSN protocols. We present the design and implementation aspects as well as limitations of this module in the current state. We eva
Electrical Engineering, Mathematics and Computer Science
Software Technology
Embedded Software