1 Introduction
In the current era, the application domain of Ubiquitous Computing and IoT has been quickly increasing as it suggests impending web connectivity for all. IoT is a system of interrelated processing devices, mechanical and computerized machines, items, creatures, or individuals that are furnished with Inique Identifiers (UIDs) and has the capacity to exchange data through a communication network without expecting human-to-human or human-to-PC connection [1]. The increasing arena of IoT prompted a technical rebellion to develop intelligent and small-sized devices, outfitted with constrained storage, energy, and computing abilities. Such advances empower fabricating huge-scale heterogeneous systems that associate an enormous number of regular physical devices like intelligent sensors, actuators, cell phones, watches, healthcare devices, RFID labels, CCITVs, wearable devices, and so on. These objects are associated with the web through a wired or wireless medium. Wireless is the most favored medium to accomplish this extensive range of connectivity. These intelligent and smart devices can detect the data, gather the data and exchange the data. The working strategy of these devices relies upon one’s requirements.
IoT has turned into a rising key innovation for forthcoming advancements, in which a heap of sensors, actuators, and brilliant questions in our dayto-day life are associated with the internet. These sensors and actuators (e.g., reconnaissance cameras, home machines, and condition observing sensors) are commonly outfitted with various types of microcontrollers, handsets, and conventions for correspondence of detecting and controlling information [2]. These genuine items, either sensors or actuators, are associated with one another to exchange their detected information to bring together servers, where data is, on the whole, put away and made accessible for specific clients with legitimate access privileges. The exchange of information from one sensor/actuator hub to another sensor/actuator hub or to an IoT server is executed over another correspondence model termed Machine-Type Communications (MTC) or Machine to Machine (M2M) [3].
Insights uncover that Internet traffic is moving from non-multimedia information to interactive media information. This predominant strength connotes the significance and increment of multimedia use in our everyday undertakings. Consistent synthesis, agreeable detecting, connectivity, and self-sufficiency on the Internet of Multimedia Things (IoMT) framework opens ways to various opportunities to enhance administrations and applications through productive usage of huge multimedia information. Be that as it may, the heterogeneous idea of huge multimedia information desires versatile and customized recommendation frameworks for effective investigation of enormous information gathered in situations like reconnaissance, retail, telemedicine, traffic checking, and disaster management. Recommender frameworks are the specialized reaction to the way that we as often as possible depend on people groups’ involvement, social standards, and provincial conventions when stood up to with another field of ability, where we don’t have a wide learning all things considered, or where such learning would surpass the measure of data people can subjectively manage. This perception, in reality, proposes that recommender frameworks are a natural and profitable augmentation, permitting both end clients and multimedia specialist organizations to play a considerably more dynamic job in choosing semantically important substance and giving significant recommendations. For example, in smart urban areas, multimedia sensors enable overseers to effectively screen resources and exercises. Upgrades in the programmed translation of media huge information can improve the limit of keen city directors via self-governing responding to crisis circumstances, and suggesting powerful activities, accordingly decreasing reaction times altogether. Moreover, novel answers for interactive media information handling and the board in the IoMT biological system can upgrade personal satisfaction, urban condition, and smart city organization.
Big data processing incorporates data management as well as data analytics. Management of data requires effective cleaning, learning extraction, and reconciliation and total techniques. Though, IoMT’s investigation depends on information modeling and understanding, which is increasingly more regularly performed by manipulating deep learning models. In a couple of years, blending ordinary and deep learning procedures, have displayed incredible guarantee in ingesting multimedia big data, investigating the worldview of exchange learning, predictive analytics, association rule mining, and so forth.
Processing elements in IoT systems
IoT application domains
IoT in healthcare domain is useful for real-time tracking and identification of both patients as well as medical equipment in health care and smart data collection. IoT sensors are real-time health indicators, which in turn help in the diagnosis of patients. IoT can also be useful for sports domain where people are concerned about getting the dynamic, real-time information of a game. In the Industrial Automation field, IoT helps in designing Smart Industrial Plants, M2M as well as Smart Plant Monitoring. If we look into Habitat monitoring, work is being carried out on Smart Agriculture, Smart Animals, and Smart Underwater Sensor Networks. To facilitate urban population growth, IoT has also emerged into applications such as Smart Buildings, Smart Environment, Smart Streetlight management systems as well as Smart Waste Management. IoT in energy management includes Smart Grid and Smart Metering. IoT also plays a vital role in Transportation for Smart Parking, Smart Traffic Congestion Detection, and Smart Logistics. The list of applications is never-ending. In all the areas of our routine life, the IoT is emerging at a great speed.
IoT World Forum Reference Model [5]
As described in [6], the features of IoMT are almost equal to those of IoT systems apart from Quality of Service (QoS) and required bandwidth. Therefore, energy-efficient solutions available for IoT applications can also be applied in IoMT domain. In this paper, we provide an exhaustive study and analysis of the latest actions to determine the Energy preservation challenges for resource compelled IoT gadgets and talk about problems and explanations given in various types of survey works. This review inspects the writing with a particular spotlight on Energy Management in terms of Efficient Communication, Routing, Self-Generating, and Recycling Energy and Energy Harvesting and Storing.
1.1 Research Problems in IoMT
The IoMT is still at an initial level of development, and numerous issues/look into challenges must be resolved before it is extensively accepted. A considerable amount of these challenges are technological, as well as interoperability, adaptability, and scalability, as a large number of diverse gadgets will be associated, yet choosing how to put resources into the IoMT is a test for corporate, and there are additionally societal, ethical, and legitimate issues, together with privacy and security of data gathering, which need to be settled.
There is a number of issues of research related to the IoMT. Some of the issues are well known now, there may be many new to emerge later on. These issues shield the entire field, comprising the technical ones of outlining, overseeing plus utilizing a multi-industrial, multinational multi-innovation framework, the commercial difficulties of creating IoMT plans of action, as well as the hierarchical, political, and societal difficulties of a novel innovation that guarantees to modify the manner of our living and working style in significant means.
The major research problems of IoMT can be categorized into Design, Scientific/Engineering, and Management/Operations categories. The design would include the concepts of Architecture, Interoperability, Scalability, Mobility, Security, and Privacy [7]. Scientific/Engineering domain includes Energy Efficiency, Power Reliability, and Robustness, whereas, Management/operations would include Software Development Availability, Data Management/Information Fusion, and Cloud Computing Performance [8, 9].
1.2 Importance of Energy Conservation in IoMT
In practical scenarios, the IoMT usage comes across numerous challenges. It is because of the devices that are comprised in the IoMT, must detect/sense information constantly [10]. Due to this constant sensing and processing of data, a large amount of energy gets consumed and hence there is a necessity for enhancing energy. An enormous number of nodes are involved in the IoMT network, and hence, it devours additional energy for the entire system [11, 12].
Energy has to be properly managed so that no node moves toward becoming energy deficient [13]. This would in turn help in making the network operational for an extensive amount of time. To achieve this, each and every node in the network of sensors ought to have executed a proficient energy management protocol.
Energy is believed to be the utmost critical asset for a sensor node, particularly if any sensor node is kept in an environment such as a distributed network and on the off chance that it isn’t possible to supply extra energy to the sensor node, as soon as the existing energy gets finished [14]. Hence, an energy management system which can adjust the demand and supply is expected, to avoid energy deficiency circumstances in the network [15]. The battery is the main controlling source of a node in sensor networks which can either be energized by recharging or changed/replaced on the basis of the environment where it has been deployed [16].
For finding out energy-efficient mechanisms, a lot of research work has been carried out. Work is being carried out on designing energy-efficient communication protocols, modifying underlying stack of communication technologies to support energy efficiency, designing mechanisms for helping the IoT devices to self-generate, store, recycle, and harvest energy [15, 17].
The main motto of our paper is to inspect the different existing mechanisms for energy conservation in IoT devices and analyze those mechanisms in terms of their efficiency. We summarize our findings to describe the advantages and limitations of existing mechanisms and provide insights into possible research directions.
2 Related Work
The scope of this survey is to first recognize the main research challenges. Numerous ongoing studies on the IoMT, incorporate an area on exploring difficulties, and we have endeavored to merge their outcomes for our motivations. This was a troublesome undertaking because of contrasts in wording by various researchers, the way that the distinctive research challenges can’t be totally isolated from one another, and the way that they can be depicted at various levels of detail. For instance, “IoT design” may be considered as a very high-level research challenge, but this incorporates a various low level of research issues, for example, “architecture”, “interoperability” as well as “adaptability” [18, 19]. Every one of these lesser level research difficulties may incorporate other lower level of research issues, e.g., for an Architectural Structure for the IoT, IEEE’s Standard incorporates the exploration difficulties of assurance, security, protection as well as safety. A few writers view IoT Standardization as a self-imbibed challenge [20, 21]. The views differ from author to author.
Some of the surveys that we have looked upon include the following: Kim and Kim [22] adjusted an Analytic Hierarchy Procedure exemplary related to three IoT usages: health-related services, energy management, and logistics, utilizing norms of innovation, showcase prospective, and regulatory situation. Review information that was examined utilizing this model demonstrated that marketplace potential was the most critical paradigm in the model’s principal layer and reasoned that the most encouraging usage from the ICT specialist’s Technical point of view is the IoT logistics. Healthcare services need to beat client obstructions and technical dependability requires to be acknowledged. Energy administration necessitates government bolster (Korean Smart Grid activity). Haghighi et al. [23] utilized a game theory methodology to deal with streamline dissemination of task and Energy utilization in the IoT networks. To deal with deciding costs for solving clashes among peers, an auction-based approach was used. The discrete-event simulation would give off an impression of being a perfect way to deal with concentrating a significant number of the outline and designing difficulties for IoT, like the scalability and energy efficiency can be developed in a situation wherein new ideas can be steadily tried. Researchers Esmaeili and Jamali [24] connected GA for enhancing energy utilization, which is a key aspect in the case of IoT networks. The authors here have designed and proposed a few more algorithms for enhancing energy utilization in the field of WSNs.
3 Methodology: Classification of Existing Energy Conservation Mechanisms
Research work is being carried out by a number of researchers for finding efficient solutions for conserving energy for IoT devices [25]. The authors in their literature have carried out a survey on the basis of different categories.
Musaddiq et al. [26] have provided a review on resource management by taking IoT Operating Systems into consideration. The authors have carried out a survey on resource management concepts of Contiki, TinyOS, and FreeRTOS which includes all the operating system concepts of process organization, memory organization, energy management, the organization of files, and communication management. Future research directions and challenges in resource management of IoT OSs are also provided.
Ryan and Watson [27] in their paper have focused on the methods of Operations Research (OR) to deal with the research challenges faced by IoT. The authors first identify the research problems, and then proposes contribution in the means of solutions where OR can be used to deal with the problem.
Abbas and Yoon [28] in their paper carried out a survey on providing solutions for energy conservation in resource-constrained IoT devices. Their main focus is on wireless networking aspects of IoT Energy Conservation. Their survey focuses on providing energy conservation solutions for devices based on their network architecture such as Wireless Networking—WWAN, WLAN, and WPAN.
Classification of existing energy conservation solutions for IoT applications
4 Solutions for Energy Management
Various techniques for conserving energy in IoT communications have been proposed by researchers [29, 30]. This section provides an overview of such existing mechanisms. These mechanisms are grouped based on their objectives and key techniques for implementation. The analysis of these mechanisms is presented in the next section.
4.1 Mechanisms for Energy-Efficient Communication
The authors of [31] have described an energy-efficient management framework for the IoT environment. The main idea proposed in this paper is to regulate the duty cycles of IoT sensors considering QoI (Quality of Information) requirements. This paper also proposes an idea to cover the critical task set to choose sensor services. This concept is based on QoI-aware sensor-to-task relevance. The advantage of this proposal is that it can be used with any underlying routing protocols for a variety of applications to preserve energy in communication. Moreover, the energy management decision is taken dynamically to deal with constraints such as service delay and optimum level of energy. The authors have also considered the latency of processing and signal propagation time and shown the impact of these factors on average measured delay probability. The proposed algorithm is greedy in nature and executes mainly three steps: shutting down sensors which are not perilous to the recent task; keeping the current status of each sensor for every task and computing the least energy requirement likelihood for task transitions. The proposed framework is applicable to certain realistic scenarios.
For energy-efficient and highly scalable IoT, the concept of multihop networking is presented in [32]. The mechanism uses blind cooperation along with multihop communications to improve scalability. Power control is necessary to have efficient blind cooperation. The authors also discuss an uncoordinated power control technique in conjunction with blind cooperative clustering to be implemented in each device. As claimed in the paper, this mechanism outperforms the simple point-to-point routing mechanism. Multihop networking helps reduce the overhead associated with the underlying routing protocols and enhances scalability. An upper bound for the mean transmit power level is computed as a function of cluster size. The proposed mechanism is evaluated based on the normalized transport rate. The performance of this mechanism is improved when there is a small deviation between the real size and assessed the size of a cooperative cluster.
According to the authors of [33], Ferroelectric RAM (FRAM) technology can be used in IoT edge devices to control unreliable power supply. FRAM is a volatile memory technology and can be treated as a unified memory. FRAM-based solutions offer reliability but they are energy inefficient in comparison of SRAM due to a greater access latency. In contrast to FRAM-based solutions, SRAM-based solutions provide a high level of energy efficiency but they are not reliable in the face of power loss. The authors present a hybrid approach which is based on FRAM-SRAM MCUs and uses shrewd memory mapping to get reliability from FRAM and efficiency of SRAM-based systems. The memory mapping technique proposed in this paper is energy-aware and lowers the consumption of energy without affecting reliability. The proposed technique uses eM-map-based representation to compute the optimum memory map for the functions which establish programs. This makes the solution platform-portable and energy-aligned. To enhance energy efficiency and performance, the solution aligns system’s powered-on time intervals to function execution bounds. The authors claim a 20% reduction in energy consumption when their proposed solution is used.
Van et al. [34] propose the use of converged Fi-Wi (Fiber-Wireless) access networks to develop a collective communication facility for the Internet of Things. The paper discusses the applicability and difficulties associated with the design and implementation of energy-efficient IoT infrastructures in the optical backhaul network. This effort discusses the usage of converged Fi-Wi networks which combine a capacity-centric OAN backhaul and a coverage-centric multi-RAT front-end network to support the Internet of Things infrastructure. The paper proposes mechanisms for power saving, scalability, energy efficiency, H2H/M2M coexistence and network integration for IoT deployment scenarios. The Authors state that in small-scale Fi-Wi-based IoT scenarios, approximately 95% of energy can be preserved by implementing TDMA-based scheduling. In the large-scale LTE-based IoT setups, up to 5 years of battery life can be attained by incorporating the suggested DRX technique.
Suresh et al. [35] describe an energy-efficient mechanism called EEIoT (Energy-Efficient Internet of Things) for IoT applications. This mechanism is based on the concept of MECA (Minimum Energy Consumption Algorithm). MECA techniques are not efficient as they do not consider energy consumption in sensor nodes. The goal of EEIoT is to control factors that energy consumption in IoT efficiently. EEIoT has a self-adaptation property and can lower energy harvesting to a great extent in the IoT environment. Different energy consumption factors have been considered by the authors to make the implementation of EEIoT effective. The paper also describes the effective method of dealing with energy efficiency requirements for data streams in big data platforms. Here, the objective is to maintain connectivity and normal functionality despite low energy levels. According to the authors, EEIoT outperforms other traditional methods of energy saving.
4.2 Mechanisms for Energy-Efficient Routing
A hybrid routing protocol for M2M sensor networks for wireless IoT applications has been proposed in [36]. In this paper, nonuniform energy consumption, scalability of network and performance degradation issues have been addressed. To handle these issues, a scalable energy-efficient clustering method is presented. In this approach, numerous mobile sink nodes have been considered for large-scale M2M sensor networks to discover the shortest routing trail to the destination node from the cluster head. It helps to lengthen the lifespan of the network. The rotation used in the selection of cluster heads results in a better distribution of energy and traffic among all sensing nodes in the network. Because of this, the lifetime of the nodes and hence of the network is increased. It also helps in improving end-to-end delay and throughput of the communication process. Simulation outcomes indicate that the use of the suggested clustering procedure and multiple mobile sink nodes enhance the network lifetime and energy distribution. The authors claim significant improvement in the routing process compared to the efficiency of the existing routing protocol.
Alkuhlani and Thorat [37] address a trade-off between the privacy of locations and energy consumption in the IoT environment. The authors of this paper suggest a secure procedure to preserve the location secrecy of the source node by modifying the routing procedure, and energy-aware load balancing protocol based on the selection of random routes to support fair energy consumption with path diversity. In this method, each packet is forwarded to a random node. To keep the actual route of the packet confidential and maximize the confidentiality, tunnels of M intermediate hops are defined. This keeps the location of the source node hidden from the attacks based on backtracking. The paper explains an energy-aware load balancing RPL-based protocol which is integrated with multiple random path-finding techniques such that each packet is forwarded in different directions on a random basis. This idea prevents attackers to eavesdrop the exact location of the source node using back-tracing and may reach a different node.
An energy proficient self-organizing multicast routing protocol called ESMR is presented in [38] for IoT applications. In this protocol, there are two categories of nodes: network nodes and nonnetwork nodes. The nodes which are present in the network are treated as network nodes. The other category of nodes, nonnetwork nodes use Markov process based diverse measurements for computing a network nodes’ weight. The protocol selects a node with the highest weight as a sink node. Nonnetwork nodes can enter into the network by sending requests to sink nodes. This is how a tree-like structure is constructed in the network. The structure of the network is balanced in stages to control energy levels. Automatic AVL tree pruning operation is used to prolong the network lifetime. When the network grows in size, the packet loss proportion does not increase due to pruning and this results in extending the lifespan of the network. In the AVL tree structure, a node is declared as a sink node only if can poise child nodes, remaining energy, hop count and the spatial deviation between the sink node and its child nodes. The height of the tree is also optimized in ESMR. The topology of the network is changed during data communication based on the future energy of sink nodes. The authors of this paper claim that a dependable tree-based network can be built using ESMR which improves the lifetime of the network and lowers the consumption of energy. The success rate of packets is shown to be improved in ESMR compared to AODV (Ad hoc On-Demand Distance Vector Routing), DSDV (Destination Sequence Distance Vector Routing), and ADMR (Adaptive Demand-Driven Multicast Routing) protocols.
Under many application areas in the IoT, the mobility of nodes and P2P (Pointto-Point) communication are the basic requirements. Therefore, such applications should have routing mechanisms that support mobility and discovery of best P2P paths and focus on energy efficiency as well. According to the authors of [39], the existing P2P routing protocols for the IoT do not upkeep the movement of nodes. Hence, they suggest a novel energy-efficient mobility-aware routing protocol titled MAEER (Mobility-Aware Energy-Efficient Routing) for the IoT scenarios. This protocol minimizes the total number of partaking nodes in the P2P path-finding process to lower the ingesting of energy. It also provides a mechanism to facilitate mobility with improved packet delivery ratio. As stated in this paper, the energy depletion of MAEER is 24% less compared to P2P-RPL protocol.
Behera et al. [40] describe the altered LEACH (Low Energy Adaptive Clustering Hierarchy) protocol to decrease the energy ingesting in sensor nodes for IoT applications. This protocol defines a threshold for selecting cluster heads with simultaneously changing the power levels between the nodes. The authors state that their modified LEACH protocol performs better than the existing LEACH and other energy-efficient protocols in terms of network lifespan, throughput, and stability period when used in different scenarios with varying network size, the density of nodes and available energy.
4.3 Mechanisms for Self-generating and Recycling Energy/Green IoT
Shaikh et al. [41] describe the efficient deployment of different technologies such as sensors, Internet, and other smart objects in the IoT environment to make it green IoT. According to the definition given in this paper, Green IoT is defined as the IoT which uses either hardware or software-based energy-efficient procedures. The objective of green IoT is to diminish the effect of the greenhouse effect of IoT services and applications. The life sequence of green IoT mainly focuses on reducing the greenhouse effect in the design, production, utilization, and disposal or cycling processes. The authors of these paper have also considered numerous facets such as applications, communications, crucial enablers, and services to achieve green IoT. The survey of various solutions for achieving green IoT is also presented. The paper provides the list of IoT application areas where it is possible to conserve energy to have the green environment. The list of key enablers of green IoT and various methods to implement energy-efficient solutions with respect to these enablers are also discussed. Domains of green IoT such as service management, heterogeneous communication, physical environments, and sensor cloud integration are required to be considered for providing efficient communication among them. Future scope in existing efforts is highlighted to implement green IoT.
An energy-efficient protocol stack named GREENNET is presented in [42]. This solution is proposed for IP-enabled wireless sensor networks but can be used in the IoT environment. The protocol stack executes on a photovoltaic cell energy-enabled hardware platform. GREENNET integrates different standard mechanisms and improves the performance of existing protocols to a great extent. It provides a discovery mechanism that facilitates the adjustment of the duty cycles of harvested nodes to the remaining energy in the network and leverages network performance. GREENNET also supports the security of standard operations at the link layer and data payload. It does not utilize multiple channels to increase the capacity of the network. Robustness and mobility of the nodes are considered in the proposed scheme.
An amended tiered clustering protocol named EH-mulSEP (Energy Harvesting enabled Multi-level Stable Election Protocol) is given in [43] for green IoT-based heterogeneous wireless sensor networks. The authors of this paper discuss the effects of energy harvesting methods in large-scale IoT systems when a large number of relay nodes that harvest energy and obtain the accumulated information from the selected cluster heads. Relay nodes forward this information to the base stations. The paper also presents a general computation method for multi-level weighted election possibility which can facilitate up to n levels of heterogeneous nodes with their corresponding level of primary energies. More than three types of nodes are considered for heterogeneity, which provides generic models for higher initial energy levels in sensor nodes. The major goal of EH-mulSEP is to reduce the energy depletion in battery-operated sensor nodes in IoT applications and maximize the conservation of energy by increasing the scalability and lifespan of the network. Using an intermediary energy harvesting layer between the base stations and cluster heads, EH-mulSEP improves the performance of the network in terms of throughput, permanence, scalability, network lifespan, and energy consumption in comparison of the other versions of SEP protocols in similar deployment settings.
4.4 Mechanisms for Storing and Harvesting Energy
IoT systems need sensing, data congregation, storage, handling, and data transmission capabilities. Real time, as well as virtual sensors, are used to provide these capabilities. Mahapatra et al. [44] describe robustness in data delivery process and energy efficiency as the major requirements of IoT communication. The authors of [44] propose data awareness, cluster head selection using active RFID tags and energy harvesting in the IoT the environment. The proposed protocol, DAEECI (Data Aware Energy-Efficient Distributed Clustering protocol for IoT), saves energy involved in the cluster head selection process. It uses active RFID tags to reduce dispensation energy by including data awareness factor and enhancing lifespan by infusing RF energy harvesting. Energy consumption models are formulated in each round and the same is sent from sensor nodes to BS through gateways. The authors claim a significant enhancement in network lifetime and data delivery when DAEECI is deployed.
A novel sensor architecture named EcoSense is presented in [45]. Unlike conventional software-based techniques, EcoSense uses a hardware-based reactive sensing technique that removes the energy waste generated by a sensor working in either standby mode or sleep mode. If the target events are available, a sensor is powered off to reserve energy. When the target events are present, a reactive connection component harvests energy from the events and activates the sensor again. Light and RF-driven sensors are used to sense lights and RF signals and provide fair reaction distances. The reactive connection module is used to control the connections between the power supply unit and sensors. By default, this module is disabled so no power is used by the sensors. When a target event takes place, the energy harvester module stores the energy correlated to the events and enables sensors by linking them to the power supply. The performance is evaluated based on reaction distance, reaction times, and working duration. Results state that the suggested mechanism is applicable only in short-range applications.
A modified routing mechanism for the 802.11 networks is presented in [46]. The routing protocol proposed in this paper uses energy harvesting data for making path-finding decisions. The objective of this modified routing protocol is to extend the network lifetime when it is set up in an energy-constrained scenario. When no viable energy source is available, network nodes harvest energy from the environment. This logic is incorporated into the routing activity so that the network operation can execute without disruptions and harvested energy can be utilized properly. The routing algorithm determines energy-efficient routes for transmitting messages through the network. To achieve this, the algorithm maintains energy harvesting information along with the level of residual energy at each hop in the network. To see the effect of the proposed routing protocol on energy management, the authors presented the simulation of the proposed logic with varying energy harvesting conditions. According to the results given in the paper, the proposed protocol can improve network lifetime by approximately 30% in low energy harvested scenarios. In high energy harvested conditions, the proposed algorithm is claimed to avert the energy hole problem successfully.
The infrastructure of the IoT comprises of a big number of battery-driven devices with restricted lifetime. The manual replacement of their batteries is not feasible in large-scale deployments. The host stations need to communicate with the distributed sensor devices and this communication requires a significant amount of energy based on the physical distance between the host and sensing nodes. An energy-efficient multi-sensing platform is presented in [47]. This paper addresses long-range device communication, energy harvesting and self-sustainability of low-power short-range devices in the network. The idea to design a power-efficient solution and reduce the quiescent current in the radio devices even when they are always on in the wireless channel. The proposed platform supports a heterogeneous long and short-range network architecture to minimize latency and energy consumption during the listening phase. For better energy management, the architecture combines LoRaTM and wake up radio. This results in increased communication efficiency and reduced power consumption.
Yang et al. [48] explore resource distribution for a machine to machine-aided cellular network to achieve energy efficiency and nonlinear energy harvesting. The proposed method uses two major access strategies, NOMA (Non-Orthogonal Multiple Access) and TDMA (Time-Division Multiple Access). This method attempts to reduce the total energy consumption in the network through joint circuit power control and time allocation. The authors state that both access strategies can be used for optimal machine communication with minimum energy consumption and improved throughput. Energy consumption of each machine type communication device is defined as a convex function with regard to the assigned communication duration. Using the optimum transmission power conditions of machine type communication devices, the optimization issue for NOMA can be transformed into an equivalent issue whose solution can be derived suboptimally. The paper also discusses the transformation of the original TDMA optimization to an equivalent tractable problem by considering appropriate variable transformation. This transformed problem can then be solved iteratively. The authors show that NOMA requires less amount of energy compared to TDMA with low circuit power control machine type communication devices. In the case of high circuit power control of machine type communication devices, TDMA does better than NOMA, in terms of energy efficiency. The paper also analyses the total energy consumed in NOMA and TDMA policies in uplink M2M communications. Energy minimization problem is stated in terms of circuit power consumption, throughput, energy causality, and transmission power constraints. Either NOMA or TDMA can be used based on the circuit power control in machine type communication devices.
5 Analysis of Existing Energy Conservation Mechanisms
Existing energy conservation mechanisms for IoT applications
Protocol | Category | Key techniques implemented | Advantages | Limitations | Approach |
|---|---|---|---|---|---|
[31] | Energy-efficient communication | QoI-aware energy-efficient framework | Transparent and compatible with lower protocols | Applicable in specific scenarios | Uses sensor-to-task relevancy and critical covering set concepts |
GREENNET [42] | Green IoT | Energy-efficient protocol stack for sensor nodes | Improved performance of existing protocols | Limited network capacity | Uses photovoltaic cell energy-enabled hardware platform |
[41] | Green IoT | Key enablers and methods to implement green IoT | Integration of IoT domains for smooth interaction | Discusses only theoretical aspects | Various application domains of green IoT |
[32] | Energy-efficient communication | Multihop networking, blind cooperative clustering | Reduced overhead in the underlying protocol, improved scalability | Efficiency depends on cluster size | Sets upper bound for mean transmit power level |
[33] | Energy-efficient communication | Hybrid FRAM-SRAM MCUs, energy alignment | Platform portability reduced power consumption | Computation complexity | Uses optimal memory maps |
DAEECI [44] | Energy harvesting | Data awareness, cluster head selection using active RFID tags | Energy saving cluster head selection, improved lifetime | None | Computes energy consumption models in each round |
[34] | Energy-efficient communication | Integrated capacity-centric OAN backhaul and a coverage-centric multi-RAT front-end network | Improved battery life | None | Uses converged Fi-Wi access networks |
EcoSense [45] | Energy storage and harvesting | Hardware based on-demand sensing technique | Energy consumption only for desired events | Useful for only short-range applications | Controlled connection between sensors and the power supply unit |
[46] | Energy storage and harvesting | Energy-aware routing protocol | Prolonged network lifetime, prevents energy hole | Cannot be used with existing routing protocols, increased computational complexity | Additional information needs to be maintained at every node |
EEIoT [35] | Energy-efficient communication | Modified MECA algorithm | Self-adaptation of energy harvesting | Specific to big data platforms | Based on MECA |
[36] | Energy-efficient routing | Scalable energy efficient clustering | Fair distribution of energy, prolonged network lifetime | None | Hybrid routing protocol for M2M sensor networks |
[47] | Energy harvesting | Energy efficient multi-sensing platform | Reduced latency, self-sustainability | None | Heterogeneous, long-range device communication |
[37] | Energy-efficient secure routing | Energy-aware load balancing routing protocol | Location privacy, fair energy consumption | Increased packet forwarding | Uses path diversity |
[48] | Energy harvesting | Circuit power control | Improved throughput, optimal machine communication | Selection of access mechanism is difficult | Considers NOMA and TDMA access mechanisms |
ESMR [38] | Energy-efficient routing | Energy-efficient self-organizing multicast routing | Prolonged network lifespan, improved packet success rates | None | Uses AVL tree pruning |
MAEER [39] | Energy-efficient routing | Energy-efficient mobility-aware routing | Supports mobility, improved packet delivery ratio | High memory requirement | Reduces the number of participating nodes for optimal route discovery |
EH-mulSEP [43] | Green IoT | Energy harvesting enabled multi-level stable election | Improved scalability, throughput, network lifetime | Uses fixed traffic patterns | Uses multi-level weighted election probability on heterogeneous nodes |
Modified LEACH [40] | Energy-efficient routing | Threshold-based cluster head selection | Enhanced throughput, network lifespan | Cannot be used with heterogeneous routing | Modification in LEACH protocol |
6 Conclusions
This paper has presented an inclusive investigation of energy preserving issues and existing mechanisms for energy-constrained IoT environment. The existing energy conservation mechanisms are classified based on the techniques used for conserving energy in battery-operated IoT devices and networks. These mechanisms have been studied and analyzed based on various aspects such as distributed and heterogeneous working environments, adjustment of duty cycles, access control, and congestion avoidance techniques, sleep time control techniques during inactivity periods, switch off and standby time of radio, resource management and scheduling, efficient cluster head selection schemes, prolonged network lifetime, throughput, scalability, and so on. As features of IoMT and IoT are almost similar, energy conservation techniques proposed for IoT systems can be used for IoMT applications to achieve energy efficiency. The survey presented in this paper evaluates the existing solutions considering various performance metrics to address energy conservation issues.