With the recent emergence of energy-harvesting technologies in wireless devices, new challenges have to be addressed by Machine-to-Machine (M2M) communication protocols. The Neutral Operation problem is a relevant problem that seeks to maintain the energy reserve of a node in a level that minimizes energy depletion and maximizes the usage of the harvested-energy. However, neutral operation in a multihop network is a more complex issue, since the nodes lack full knowledge of the network and the nodes have diverse harvesting and consumption profiles. A simplification of the Neutral Operation problem is proposed, named Neutral Operation of the Minimum Energy Node, in which the node with the lowest amount of energy determines the operation of the whole network. This paper proposes a battery-aware solution, called Routing and Aggregation for Minimum Energy (RAME), that performs data-aggregation on the traffic load according to the minimum energy reserve on the path. As part the proposed solution, a kinetic battery model has been developed to provide non-linear battery level estimation. Besides, the Routing Protocol for Low-Power and Lossy Networks (RPL) was enhanced to use the kinetic battery estimation as metric for parent node selection and to find periodically the minimum energy reserve on the available paths. The performance evaluation of the proposed mechanism using Contiki shows the benefits of RAME in comparison to the M2M standard protocols.

Machine-to-Machine (M2M) communications have emerged as a new concept for the next generation of sensing and actuating systems. With the recent emergence of energy harvesting technologies, the current communication solutions have addressed the problem of controlling the data communication to regulate efficiently the energy consumption, aiming to avoid under and overuse of energy. However, these solutions do not consider data aggregation as means of controlling the network traffic. To fill this gap, this paper proposes the Data Aggregation for energy harVesting NETworks (DAV-NET), which regulates the energy consumption in accordance with the residual energy stored in the batteries, exploiting the data aggregation capabilities to control the network traffic. The performed simulations show that the proposed solution is able to regulate the energy consumption in case of abundant or scarce energy, controlling the aggregation level in a distributed fashion.

Machine-to-Machine (M2M) communication is an emerging paradigm for the next generation of sensing and actuating systems. Energy harvesting technologies applied in sensor and actuator devices must be considered by the communication solutions in order to achieve energy-efficient M2M communication. This paper addresses the problem of regulating the energy consumption in accordance with the harvested energy. Thus, it proposes Data Aggregation and communication Periodicity control for Energy Harvesting (DAPEH), a solution that controls the network energy consumption adjusting the Data Aggregation level and the periodicity of the communication. Data Aggregation allows DAPEH to control the amount of traffic sent over the network, while the communication periodicity enables the regulation of data production. The evaluation of the proposed solution uses real measurements of solar harvested energy and shows how the network can increase or decrease the energy consumption to avoid under and overuse of energy.

The energy resources of Machine-to-Machine (M2M) devices need to last as much as possible. Data aggregation is a suitable solution to prolong the network lifetime, since it allows the devices to reduce the amount of data traffic. In M2M systems, the M2M platform and the Constrained Application Protocol (CoAP) enable multiple entities to send concurrent data-requests to the same capillary network. For example, in a Smart Metering scenario, there are devices measuring the electricity consumption of an entire building. The supplier company requests all devices to send the data updates every 1800 seconds (i.e., 30 minutes). On the other hand, a resident requests his/her devices to communicate every 600 seconds (i.e., 10 minutes). These concurrent data-requests create heterogeneous groups over the same capillary network, since each group might be able to execute different in-network functions and to have a unique temporal-frequency of communication. However, the traditional data aggregation solutions designed for periodic monitoring assume the execution of a single static data-request during all network lifetime. This makes the traditional data aggregation solutions not suitable for M2M environments. To fill this gap, this paper presents Data Aggregation for Multiple Groups (DAMiG), which is designed to provide Data Aggregation for heterogeneous and concurrent sets of CoAP data-requests. DAMiG explores the group communication periodicity to perform internal and external-group traffic aggregation. To achieve that, DAMiG computes a suitable aggregation structure and applies statistical and merger aggregation functions along the path. DAMiG is able to reduce the energy consumption in scenarios with single or several concurrent CoAP data-requests. Moreover, the selection of internal and external-group paths takes into account the residual energy of the nodes, avoiding the paths with low residual energy.

Machine-to-Machine technology supports several application scenarios, such as smart metering, automotive, healthcare and city monitoring. Smart metering applications have attracted the interest of companies and governments since these applications bring many benefits (e.g. costs reduction and increased reliability) for production, monitoring and distribution of utilities, such as gas, water and electricity. Multi-hop wireless communication is a cost-effective technology for smart metering applications because it extends the wireless range and enables fast deployment. Smart metering data communicated via wireless multi-hop approaches needs mechanisms that makes the communication less vulnerable to security threats and saves the device resources. Data encryption and data aggregation mechanisms emerge as potential solutions to fulfill these requirements. However, the simultaneous execution of data encryption and data aggregation mechanisms is not a trivial task. This is because the data encryption prevents the data aggregation mechanism to summarize the data along the path. Another challenge is to manage both mechanisms according to the concurrent Machine-to-Machine (M2M) applications interests. In this context, we present sMeter, which is a framework that deals with multiple applications interests, avoiding interest conflicts of concurrent users and supporting the management of data aggregation and data encryption. sMeter is implemented using low-cost hardware in an indoor environment. The communication is performed via a wireless multi-hop technology, and the performance of this communication is evaluated in terms of delay, data reception ratio and received signal strength indication.