Recent Advances in Green Internet of Things Claudio Estevez, Jinsong Wu Electrical Engineering Department Universidad de Chile Santiago, Chile 8370451 Email:
[email protected],
[email protected] Abstract—Environmental issues are acquiring more attention as the general public becomes more aware of the consequences that the environment degradation is capable of causing. This has triggered various initiatives, i.e., the IEEE Comsoc approved the technical committee of Green Communications and Computing, which aims at having a special task force focused on environmentally-friendly communication topics. In this work, some of the most recent advances in this field are discussed. The topics include energy efficiency, energy harvesting, and solutions to pollution issues all in the context of the Internet of Things (IoT), which is expected to have a great impact on the green topics. Keywords: Green, IoT, energy efficiency, energy harvesting, pollution reduction
I.
I NTRODUCTION
Environmental issues are acquiring more attention as the general public becomes more aware of the consequences that the environment degradation is capable of causing. As a result, various initiatives have been born to counteract the effects caused by our own technology. Green topics are inherently multi-disciplinary, which may include energy efficiency, energy self-sustainability, environmental sustainability, and others. As discussed and defined in [1], green topics include not only energy or energy efficiency issues, but also the broader context of environmental impact and enablement of sustainability. The Internet of Things (IoT) represents huge breakthrough in the evolution of computerized interconnectivity. This vast pervasive network not only includes traditional computers, but also a whole new breed of smart autonomous devices that seamlessly interconnect via machine-to-machine (M2M) communications to provide a whole new set of benefits, including many types of services. The range of locations is basically unbound, as IoT devices can be in vehicles, buildings, habitats, humans, utility grids, containers, garments, smart phones, plants, even underwater (as discussed ahead). These may impact environmental and operational states. Since IoT may ubiquitously exist, they may have the great potentials to aid the green objectives. The paper will intend survey some recent research efforts and results on Green Internet of Things, which may encourage more future relevant investigations and applications to support the sustainable world. II.
G REEN I OT T OPICS OF I NTEREST
For the convenience of discussions, we classify the relevant works as the following categories: energy efficiency, energy self-sustainability, and pollution control.
A. Energy Efficiency Energy efficiency is one of the broadest topics covered in the green spectrum. Energy conservation indirectly contributes to the reduction of the carbon fingerprint. In many cases there are additional advantages such as cost reduction, greater device independence, among others. Since the energy efficiency topics are quite broad, the relevant subsection is divided into the following subsubsections: M2M and smart city, energy management, and protocol and scheduling. 1) Machine-to-machine and Smart City: The first set of topics presented are related to machine-to-machine (M2M) and Smart City, as these topics are the most palpable and applied. This projects immediately the advantages of implementing Green IoT under various scenarios. From a telecommunication operator standpoint energysaving solutions can bring many benefits. This work [2] described energy-saving solutions for telecommunication operators using the IoT. There are a significant number of various equipments in a telecommunication operator enterprise, therefore reducing the energy consumptions would have a beneficial impact to the environment and also a reduction in energy costs. The solution can be described to have two parts: (1) Monitoring: A real-time comprehensive monitoring system to collect energy consumption information focused mainly on the base stations. (2) Control: Based on the energy state of the equipment (mainly base stations) there is a linkage between the monitoring devices and the power management system. The objective is straightforward, make intelligent decisions based on the environment state. The proposed role of the IoT is classified into three categories: (1) Energy measurements: The measurements can not only feedback to provide real-time feedback and control, but also to gather statistics and therefore make smarter long term decisions like a preemptive load balancing. (2) Remote monitoring and control: Through the use of remote monitoring devices, connected to the operator via the cellular network, the equipment condition can be monitored (such as temperature, humidity, light, and so on.) to obtain feedback that could affect how the power consumptions are distributed. (3) Intelligent linkage: By intelligently linking the environment information provided by the IoT to the management system, the operator’s equipment can perform optimally and safely via controlling the environment system, without overdoing it, with the exact amount of energy necessary. With these tools it is possible to reduce unnecessary equipment energy consumption. The work of [3] raises an interesting topic, which is the energy consumption of the sensors in IoT. Some research work analyze the benefits of energy savings by discussing the energy
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consumption with and without the presence of IoT sensors, but, when discussing the benefits of having IoT, it is often not mentioned that of the energy required to run the IoT network itself, which usually consists of a massive amount of low-powered devices. The proposed solution for the case of a massive distributed IoT sensor network, is to have a coordinated effort, although relevant strategies have been also discussed in [4]. The key to the energy efficiency is the scheduling algorithm [3]. The scheme requires the time to be slotted and the nodes decide to be active or not based on the information gathered from its neighbors, which are constantly transmitting their activity decision [3]. The successful synchronization allows for the nodes to consume energy only when necessary, hence saving this valuable resource. This solution, though, assumes a very specific architecture, that is, massive distributed wireless nodes with overlapping cells, which may not be the case for all (or even most) types of networks. Moving on to a bigger picture, we will discuss the potential energy savings in a IoT-enabled Smart City [5]. Improving the efficiency of city services and facilitating a more sustainable development are some of the main drivers of the Smart City concept. Here a case was presented in which the city of Santander, Spain, used IoT devices to sense the environment and make smarter decisions. In this trial, the indicators of interest where energy related and the sensing environment consisted of sensors that detect the presence of people and cars, and the controlled infrastructure is the street lighting. The lighting is programmed to dim when no activity is detected, and the intensity of the light varies depending on various factors, such as the detection of a pedestrian, a car, rain, and other. Sensors not only detect presence, but also light intensity, such that the street lighting can be adjusted under cloudy conditions, even during the day, if necessary. This last factor increases the energy consumption during the day (if dark conditions are met), nevertheless this energy is minimal compared to the energy consumed during the night. The tests reported in this work were performed during the night, in clear and rainy conditions. As expected the energy savings were significant, the energy consumed in the IoT-enabled system is approximately one third of the energy consumed before the trial. Additionally it has been reported that the CO2 emissions was lowered. 2) Energy Management: Energy management is an important subject. The efficiency of the management determines greatly the energy efficiency of the entire system. Here we discuss some practical and theoretical works. In the work [6], a trade-off problem was discussed, bandwidth versus energy. To manage the energy resources, a cooperative differential game model was proposed. The game model is based on the work [7], [8], where the layers are the attributes of interest, in this case bandwidth and energy (i.e., only two players). The steps to find the fair resource allocation solution are: (1) Computing the optimal cost of grand coalition, the feedback Nash equilibrium, and the optimal cost for intermediate coalitions. (2) Calculating Shapley value and allocating the total cooperative payoff. And (3), allocating over time of each player Shapley value. With these steps it is possible to find the optimal point that minimizes the resource consumption. The results reflect that as the bandwidth increases the energy decreases, and vice-versa.
Considering physical sensors with certain sensing capabilities in an Internet-of-Things (IoT) sensory environment, in this work [9] it was proposed to design an efficient energy management framework to control the duty cycles of these sensors under quality-of-information (QoI) expectations in a multitask-oriented environment. The QoI is characterized by a number of attributes including accuracy, latency, and physical context (specifically, sensor coverage). There are various technical challenges in sensor energy and data quality management. A major one that drives this work involves the large-scale management of heterogeneous devices that are expected to populate IoT systems. To address this challenge, an energy management service (and supporting algorithms) that is transparent to and compatible with any lower layer protocols and over-arching applications is designed, while providing longterm energy-efficiency under the satisfactory QoI constraints. To achieve this design it is necessary to introduce the concept of sensor-to-task relevancy to explicitly consider the sensing capabilities offered by a sensor to the applications and QoI requirements required by a task. Then, a generic information fusion function is presented to compute the critical covering set of any given task in selecting the sensors to service a task over time. Finally, a runtime energy management framework based on the previous design elements is proposed to control the duty cycles of a sensor, where the control decision is made optimally considering the long-term task usage statistics and the service delay of each task serves as the constraint. To demonstrate the effectiveness of the management system a scenario with 15 sensors and 4 tasks was created where the sensors can cover anywhere between one to four tasks, the goal was to distribute the energy consumption as evenly as possible. Results have shown that the sensors that covered four tasks where able to delegate tasks efficiently to those that covered less tasks, hence having significant energy savings. Looking at an applied case, the work in [10] raised an interesting question. Even with feedback control of a particular amenity, for example heating, that is set to turn off if the temperature reaches a certain value. Is this energy efficient if there is no one in the room, house, our building? This work incorporates a location-based feedback that controls the energy modes of various appliances, putting them into energy-saving mode when a user is leaving the building/home/office, and preheats/cools if the user is approaching. Using a LEED-goldcertificated green office building, a unique IoT experimental testbed was built for the energy efficiency and building intelligence data collection. For one year the building was monitored. The results have shown that, due to the centralized and static building controls, the actual running of green buildings may not be energy efficient even though they may be ”green” by design. An IoT framework with smart location-based automated and networked energy control, which uses smartphone platform and cloud computing technologies to enable multi-scale energy proportionality including building-, user-, and organizational-level energy proportionality demonstrates to give good energy efficiency. It was shown in a proof-of-concept IoT network and control system prototype that carries out realworld experiments which demonstrated the effectiveness of the proposed solution. The broad application of the proposed solution has not only led to significant economic benefits in term of energy saving, home/office network intelligence improvement, but also bought in a huge social implication in
terms of global sustainability. 3) Protocol and Scheduling: The topic of protocol and scheduling is critical at the lower levels, in both communication and computing. This work is generally more theoretical and simulation based, as the work presented here shows. Here two scheduling and two protocol topics are discussed. Unlike existing power saving protocols of human-to-human (H2H) networks, H. Chao et. al. in [4] emphasized the importance of designing a dedicated M2M power saving mechanisms. Energy efficiency is an important trait that is gained from a more innate M2M network. Unlike human to human terminals, a lot of machines related to specific M2M applications are fixed and not expected to move randomly, and further, the M2M communications timings are also less critical than those of H2H communications. The solution consists on improving power savings for both M2M devices and network operations. Removal of unnecessary M2M activities will be beneficial to conserve the power of M2M devices. On the operation side, improvements in operations and optimized signaling flow were also proposed, which may help reduce the power consumption of M2M devices indirectly. Even though there were no exact computations of energy savings, the work does present a significant reduction in communications activities due to the mechanism migration from H2H to M2M. In another relevant work[11], energy efficiency was addressed in these types on networks, more specifically LTE (Long Term Evolution) Advanced (LTE-A). For IoT applications, continuous low-rate streaming data may be reported from devices over a long period of time, imposing stringent requirements on power saving. To manage power consumption, 3GPP LTE-A has defined the discontinuous reception (DRX) and transmission (DTX) mechanisms to allow devices to turn off their radio interfaces and enter sleep mode in various patterns. Existing literature has paid much attention to evaluate the performance of DRX/DTX; however, how to tune DRX/DTX parameters to optimize energy cost is still on-going research. In [11], the DRX/DTX optimization is addressed by maximizing the sleep periods of devices while guaranteeing the quality-ofservice (QoS), specifically on the aspects of traffic throughput, packet delay, and packet loss rate in IoT applications. Efficient schemes to optimize DRX/DTX parameters and schedule packets at the base station are proposed. Simulation results have shown that these schemes can guarantee the aforementioned QoS attributes while saving the energy of the device. Another scheduling work was described in [12]. Here the network is more generic than the previous case. It is somewhat comparable to a sensor network. The nodes in this topology have different hierarchies, basically sensor nodes and, what are referred to as, brokers. The brokers act as an intermediary between the sensor nodes and the sink (edge node). The authors described that, in earlier works, the scheduling had performed solely based on expiration times, but claimed to improve the performance by taking into consideration the overall IoT system efficiency. Additionally, the routing algorithm is also a energy saving feature of the proposed system. From the results obtained, it can be observed that the proposed scheduling and routing algorithms provide better energy efficiency and response times. It should be mentioned that improving the response time is what causes the savings in energy.
Internet of Things (IoT) envisions the notion of ubiquitous connectivity of everything. However, the current research and development works have been mostly restricted to scalar sensor data based IoT systems, thus leaving a gap to benefit from services and application enabled by the Internet of Multimedia Things (IoMT) [13]. Recently, IETF ROLL working group standardized an IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) for resource constrained devices. RPL builds a tree-like network topology based on some network metric optimization using RPL Objective Functions[13]. Previous RPL implementations for scalar sensor data communication are not feasible for IoMT, since multimedia traffic pose distinct network requirements. The goal of this work is to design an enhanced version of RPL for IoMT in which the sensed information is essentially provided by the multimedia devices. The proposed RPL implementation incorporates application specific Quality of Service and energy requirements. The performance is highly dependent on the link quality. If the link is poor, the effective throughput would drop and the amount of packet delivered per unit time would decrease, in which case the energy must be increased. If the throughput requirement is been met, the energy can be reduced to a level where the QoS requirements are still met. The proposed protocol, referred to as Green-RPL, is compared to two techniques (ETX and OF0). Results have shown that it is able to outperform OF0 in energy efficiency and throughput, nevertheless, when compared to ETX, in outperforms it in terms of throughput but not in energy efficiency. The work [14] mainly has shown that the IoT can reach places that many would not imagine. As with the previous case, an energy efficient protocol was discussed, nevertheless this protocol was optimized for underwater operation. For the reader that is unfamiliar with underwater wireless sensor networks (UWSN), this is a field mainly to explore the vast ocean space. This work proposed an Enhanced Channel-aware Routing Protocol (ECARP), which is an improved version of the existing CARP protocol. The main contribution of this work over its predecessor is the improved energy savings. It is able to achieve this in two ways: (1) Improved data transmission: When the sensor measurement does not change (within a certain threshold) the protocol transmits a much smaller packet that only informs that the value remains the same rather than retransmit the whole data packet. (2) Improved routing: rather than constantly sending routing control messages to constantly determine the best path, a memory is implemented so that it selects only among previous successful paths, reducing the amount of signaling between nodes. Results show that for the simulation parameters used that ECARP uses 25% of the energy used by CARP to perform the same tasks. B. Energy Harvesting Energy harvesting is well established as one of the prominent enabling technologies for the pervasive development of the IoT. Energy harvesting is one of the main contributors to energy independence, also commonly referred as energy self-sustainability. When true independence is reached, the only obstacle remaining is battery life, nevertheless, in theory, devices could potentially be self-sustainable for many years. In [15] a simple wireless node was built using a solar panel energy harvesting source, lithium batteries for stable
power, various sensors, and a processor to program the energy management. The processor used was the S3C6410 ARM11, which is a low-cost, low-power, high performance microprocessor. Some external interfaces used in this testbed are RFID readers, infrared sensors, environmental sensors, and multi-channel sensors. The wired communication interfaces supported include RS485, RS232, USB, and Ethernet, and the wireless communication interfaces supported are composed of GSM, GPRS, CDMA, Zigbee, WiFi, and Bluetooth. For the test performed in this work only Ethernet and Zigbee where tested. For the energy collection and management system a solar battery board is used alongside a 25 F super capacitor, which collects the energy generated by the solar panel. For a stable energy source, lithium batteries are used. For the test, the terminal carries out a regular transmission. The transmission lasts 3 seconds and it is repeated every 5 minutes. Using these experimental setup parameters the terminal is able to maintain self-sustainability for a long period of time (not specified). This work [16] summarized various energy harvesting methods oriented for IoT, which implicitly means that the energy collectors are generally small in size. One of the energy harvesting devices is called the hybrid solar-rectenna. It is a device that is equipped with a small solar panel and an antenna that acts much like a rectifying circuit, converting an analog AC (alternate current) signal into a DC (direct current) signal. Once in DC form it can be used to power small low-power devices. The energy harvested is also combined with the energy harvested from the solar panel, making this little device more convenient. Another prototype worth mentioning is an energy harvesting system composed mainly of piezoelectric parts that can be installed in shoes, is able to collect energy from the user when walking. The prototypes mentioned here can be inserted in clothing and optimistically power e-Health sensors and actuators, such as blood pressure sensor, insuline pump, ECG sensor, EMG sensor, motion sensor, etc. The building blocks for the IoT are the sensors, which by incorporating energy harvesting devices become allows for independent nodes increasing the pervasiveness. Many types of energy harvesting devices are been developed, however, there have been still only very limited understandings in the properties of various energy sources and their impact on energy harvesting adaptive algorithms. In [17], the work was focused on characterizing the kinetic (motion) energy that can be harvested by a wireless node with an IoT form factor and on developing energy allocation algorithms for such nodes. This work also described some methods for estimating harvested energy from acceleration traces, and studied energy generation processes associated with day-long human routines, such as walking, running, cycling, etc. These observations provided insights into the design of motion energy harvesters, IoT nodes, and energy harvesting adaptive algorithms. Data were collected using accelerometers and estimating the energy harvested, but there do not seem to be a direct measurement. The estimated energy harvested walking is in the order of 200 μW and running is 800 μW . These are significant power values are could easily power low-power body sensor networks. Energy self-sustainability is a very desirable attribute in IoT sensing devices, arguably it is an indispensable attribute. In the work [18], a wireless energy harvesting system for IoT (WEH-IoT) is described. In the proposed system, the antenna
is able to not only receive data, but, when not in use, it is able to absorb the EM (electromagnetic) radiation to charge the device, or at the very least extend the time between charges. The system proposed to have a rectifying component near the antenna connection that converts AC (more specifically RF(radio frequency)) to DC power. Once in the DC domain it can be added to the power supply. This harvesting system can extend the life-time of the battery, particularly when the energy consumption is relatively low. C. Pollution Control Pollution is the consequence of existing technologies, energy inefficient systems have a greater toll on the environment degradation. The solutions discussed here aim mainly at monitoring the pollution, the control is not yet automated. It can be said the the feedback loop is completed by humans. In [19], it has claimed that to properly implement an energy-saving and emission-reduction (ESER) policy, it is fundamental to perform an effective quantitative evaluation of ESER. Current ESER evaluation technology is isolated from the enterprise information systems, such as research planning, product data management, and customer relationship management. To address this problem [19] proposed a novel method for ESER life cycle assessment based on an IoT system and the bill of material (BOM). Some issues that the current ESER evaluation system encounters are: (1) The data required in existing assessment methods are collected manually, and the assessment relies primarily on existing data. It is required to have real-time and dynamic requirements, which are not met. (2) Current studies focus on environmental and process industries, and treats these entities as a unit. The data yields information regarding the entity as a whole, but no details are collected at different levels or stage of production incapacitating the ability to optimize these further. (3) ESER assessment tools are independent from the existing enterprise information systems, hindering the integration and interaction of these. (4) It uses client/server software architecture, which can be arduous to upgrade and maintain. The solutions proposed are obtained in a straightforward manner from the weaknesses, and these are: (1) Meet real-time and dynamic requirements by using IoT, (2) Study BOM-based evaluation theories/methods for a multi-structure and multilevel ESER evaluation, (3) Have the ESER evaluation done in existing enterprise information systems, and (4) switch to a browser/client software architecture, which is OS independent, easily upgradable, and has lower maintenance costs. Another work related to air pollution in discusses in [20]. Online automatic monitoring system of malodor pollution, which can perform real-time, automatic monitoring and risk assessment, was designed to improve the handling ability of malodor emergency alerts. In this work, remote sensing monitoring odorous pollutants diffusion, assessment and prewarning for emergencies were studied utilizing the ArcGIS database with the real-time data monitoring of malodor, meteorological parameters, and other tools. The data center of the monitoring system was structured on a Hadoop cloud computing platform. Relying on the environmental protection supervision, cooperative supervision is achieved, which was supported by IoT for large-scale monitoring networks. A test trial was developed in Dagang Industrial Park of the Tianjin
Binhai New Area. The system has proved that it directly enriches the environmental IoT enhancing the malodor monitoring level that, consequently, improves the environmental supervision capability in China. Yet another example of environment monitoring can be seen in [21]. Here a solution, called ekoNET, was developed for real-time monitoring of air pollutants and other weather indicators, such as temperature, pressure, and humidity. ekoNET is based on low-cost sensors, which are simple to deploy, use, and maintain. This initiative was intended to be used in the context of the IoT-domain of Smart Cities. ekoNET devices are equipped with GPS and GPRS communication capabilities, as well as CO2 , O3 , N O − B4, N O2 − B4, and CO − B4 concentration sensors. The ekoNET system can help target the location of environmental air-related issues that may arise in a Smart City. III.
[7]
[8]
[9]
[10]
[11]
[12]
C ONCLUSION
It has been shown here that IoT unquestionably has the potentials to cause a great positive impact on the care of the environments. On the broad sense, Green IoT can may improve energy efficiency themselves and that of other systems, help reduce environmental pollution. With the aid of energy harvesting IoT systems can become independent and scatter further into more inaccessible locations helping in this way monitor greater portions of our environments. Exciting topics like automated city lighting, device activation using user-location-based criteria, and energy independent nodes. Promising energy efficient protocols and scheduling techniques point toward even greater energy savings in the future. Energy harvesting is making energy independence a reality and pollution control is becoming smarter and more pervasive. All these works point toward greener environments.
[13]
ACKNOWLEDGMENT
[18]
This work is partially funded by project FONDECYT 11121655.
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