![Energy Management Architecture tion 3 examines in greater depth how applications might benefit from this architecture. To provide a basis for energy management, we suggest an architecture that allows sensornet application writers to treat energy as a fundamental design primitive. Building systems that manage energy as a critical resource is not a new concept; research in a number of areas harnesses this idea. In fact, our architecture incorporates many of these concepts, including classifying energy as a first-class OS resource [26], prioritizing resource requests [2], accounting for fine-grained energy consumers [19], allocating resources based on dynamic constraints [7], and providing quality-ofservice (QoS) guarantees by using feedback [13]. In addition, we adopt a three component decomposition that is common for architectures managing scarce shared resources, seen in Figure 1: (1) a policy interface for user input, (2) a mechanism to monitor and control system and component resource usage, and (3) a management module for enforcing policy directives. Section 2 describes the specific roles of these components in our architecture. By employing and adapting concepts from traditional OS, networking, and architecture literature, we envision an energy management architecture (EMA) that allows sensornet applications to accurately view and efficiently manage the energy budget of a node. The primary contributions of this work beyond existing architectural efforts are facilities for: (1) individual accountability for management of com](https://www.researchgate.net/profile/David-Culler-4/publication/234759933/figure/fig1/AS:670002965405702@1536752563235/Energy-Management-Architecture-tion-3-examines-in-greater-depth-how-applications-might_Q320.jpg)
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An architecture for energy management in wireless sensor networks
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... Distributed estimate using wireless sensor nodes provides several intriguing issues, including the apparent restrictions of transmission capacity, which contribute to quantization error in addition to a disturbance in the transmitted signal. Another significant limitation is the poor battery power of the detecting nodes themselves [2]. Then, a WSN estimate paradigm that provides adequate efficiency while may be energy efficient is sought to extend the WSN's lifespan. ...
The wireless sensor network (WSN) approach is one of the fastest growing approaches in the world of communications and engineering. The primary objective of a WSN is to discover the important information about the environment, depending on the nature of the applications under which it is implemented, and to communicate this information to a single base station (BS) so that appropriate measures can be taken. These sensor nodes communicate via a variety of protocols. The difficulty with the traditional system is that while collecting the observed data, each node transmits its felt information directly to a base station, which quickly exhausts its power. This study suggests a Backbone Energy-Efficient Sleeping (BEES) management strategy with two appealing features: (i) the capacity of backbone is scalable by basic parameters, and (ii) the backbone nodes were distributed equally, implying that the backbone on its own is energy efficient during routine activities. Reliable connections are expected to obtain QoS and routing protocols of such backbone nodes in wireless multihop systems. As a result, present localized routing in virtualized backbone schedule cannot ensure energy-efficient paths. An energy-efficient routing scheme for Virtual Back Bone Nodes (VBS) increases life of node and switches off its radio while in sleep state to spend less power. BEES’ performance is evaluated by comparing it to two different topology management techniques. The results show that BEES performs better algorithms. It ensures optimal routing with minimal node power consumption but also implements the essential communication range for backbone networks.
... There are many challenges like comprehensive sensing, detecting data spatial and temporal correlation, proper resource management, distributed and adaptive machine learning techniques must be developed for in-network processing of data instead of exhausting the nodes for high computational tasks. We have referred to a few studies that discuss these issues [129][130][131][132][133][134][135][136] and summarized in Table 3. ML-based Data compression and dimensional techniques reduction can be used to compress the data instead of traditional compressed techniques to produce a better energy-saving scheme. Data correlation is an important issue that needs to be addressed in hierarchical clustering. ...
Energy conservation is the primary task in Wireless Sensor Networks (WSNs) as these tiny sensor nodes are the backbone of today’s Internet of Things (IoT) applications. These nodes rely exclusively on battery power to maneuver in hazardous environments. So, there is a requirement to study and design efficient, robust communication protocols to handle the challenges of the WSNs to make the network operational for a long time. Although traditional technologies solve many issues in WSNs, it may not derive an accurate mathematical model for predicting system behavior. So, some challenging tasks like routing, data fusion, localization, and object tracking are handled by low complexity mathematical models to define system behavior. In this paper, an effort has been made to provide a big outlook to the current “researchers” on machine learning techniques that have been employed to handle various issues in WSNs, and special attention has been given to routing problems.
... In recent years, scientific and industrial adoptions of WSNs are becoming more pronounced and predictability and longevity are of paramount importance in most applications. Although, many work have sought after balancing lifetime requirements and network performance, however, due to unexpected and unpredictable environmental dynamics, these networks still suffer from premature energy drainage even for network with harvesting and recharging capabilities [2]. Reconciling these two conflicting objectives is commonly achieved via energy management. ...
Energy in wireless sensor networks is considered a scarce commodity especially in scenarios where it is difficult or impossible to provide supplementary energy sources once initial available energy is used up. Even in cases where energy harvesting is feasible, effective energy utilization is still a crucial step for prolonging the network lifetime. Enhancement of lifetime through efficient energy management is one of the essential ingredients underlining the design of any credible wireless sensor network. In this paper, we propose a sensor selection method using a novel and unsupervised neural network structure referred to as Partly-Informed Sparse Autoencoder (PISAE) that aims to reconstruct all sensor readings from a select few. PISAE comprises of three submodules namely: the gate (which selects the most important sensors), encoder (encodes and compresses the data from select sensors), and decoder (decodes the output of the encoder and regenerates the readings of all initial sensors). Our approach relies on the premise that many sensors are redundant because their readings are spatially and temporally correlated and are predictable from the readings of few other sensors in the network. Thus, overall network reliability and lifetime are enhanced by putting sensors with redundant readings to sleep without losing significant information. We evaluate the efficacy of the proposed method on three benchmark datasets and compare with existing results. Experimental results indicate the superiority of our approach compared to existing approaches in terms of accuracy and lifetime extension factor.
... In either case, these activities can be further considered as accidental side effects or results of nonoptimal software and hardware implementations (configurations). For example, observations based on field deployment reveal that some nodes exhausted their batteries prematurely because of unexpected overhearing of traffic that caused the communication subsystem to become operational for a longer time than originally intended [43]. ...
... Consequently, the restricted energy resources primarily available will be reduced and will impede the operation of the whole network. Overviews and surveys have been made of the main techniques of energy, such as Munir and Gordon-Ross, 13 Jiang et al., 14 and Boudhir et al., 15 focusing on the Dynamic Power Management (DPM), such as Dargie, 16 Paul, 17 Bogliolo et al., 18 Lin et al., 19 and Sinha and Chandrakasan, 20 and DPM with Scheduled Switching. 21 The use of DVFS in WSNs has been discussed by Kulau et al. 22 and Yuan and Qu. ...
Over the past few years the applications for Wireless Sensor Networks (WSNs) have grown at an ever-increasing rate. However, the evolution of those networks has been reduced by the energy scarcity. It retards the development of the WSN performances required while exploring new applications and improving the WSN potential. Besides, in order to design energy-efficient solutions, it is important to take into account the power dissipation due to noncompliance with time constraints. As a result, we will provide a model of power management that will be simulated and validated by the STORM Simulator (Simulation TOol for Real time Multiprocessor scheduling). However, unlike traditional WSN energy management systems, our power manager reduces the energy consumption through a dual approach: a global and dynamic approach using the analysis of the behavior of the network and a local one applied at the node level. We have relied on energy optimization techniques to yield extensive lifetime for every node battery and mainly both Dynamic Power Management and Dynamic Voltage and Frequency Scaling, which are appropriate for the WSN. This model will be based on a global Earliest Deadline First scheduling policy. Besides, we aim to extend the STORM simulation tool to include those power management techniques.
... In either case, these activities can be further considered as an accidental side-effects or results of non-optimal software and hardware implementations (configurations). For example, observations based on field deployment reveal that some nodes exhausted their batteries prematurely because of unexpected overhearing of traffic that caused the communication subsystem to become operational for a longer time than originally intended (Jiang et al., 2007). Similarly, some nodes exhausted their batteries prematurely because they aimlessly attempted to establish links with a network that had become no longer accessible to them. ...
High efficient routing is an important factor to be considered in the design of limited energy resource Wireless Sensor Networks (WSNs). WSN environment has limited resources in terms of on-board energy, transmission power, processing, and storage, and this prompt for careful resource management and new routing protocol so as to counteract the challenges. This work first introduces the concept of wireless sensor networks, routing in WSNs, and its design factors as they affect routing protocols. Next, a comprehensive review of the most prominent routing protocols in WSN, from the classical routing protocols to swarm intelligence based protocols is presented. From the literature study, it was found that comparing routing protocols in WSNs is currently a very challenging task for protocol designers. Often, much time is required to re-create and re-simulate algorithms from descriptions in published papers to perform the comparison. Compounding the difficulty is that some simulation parameters and performance metrics may not be mentioned. We then see a need in the research community to have standard simulation and performance metrics for comparing different protocols. To this end, we re-simulate different protocols using a Matlab based simulator; Routing Modeling Application Simulation Environment (RMASE), and gives simulation results for standard simulation and performance metrics which we hope will serve as a benchmark for future comparisons for the research community. Also, from the literature study, Energy Efficient Ant-Based Routing (EEABR) protocol was found to be the most efficient protocol due to its low energy consumption and low memory usage in WSNs nodes. Following this efficient protocol, an Improved Energy Efficient Ant-Based Routing (IEEABR) Protocol was proposed. Simulation were performed using Network Simulator-2 (NS-2), and from the results, our proposed algorithm performs better in terms of energy utilization efficiency, average energy of network nodes, and minimum energy of nodes. We further improved on the proposed protocol and simulation performed in another well-known WSNs MATLAB-based simulator; Routing Modeling Application Simulation Environment (RMASE), using static, mobile and dynamic scenario. Simulation results show that the proposed algorithm increases energy efficiency by up to 9% and 64% in converge-cast and target-tracking scenarios, respectively, over the original EEABR and also found to out-perform other four Ant-based routing protocols. We further show how this algorithm could be used for energy management in sensor network in the presence of energy harvesters. However, high number of control packets is generated by the IEEABR due to the proactive nature of its path establishment. As such, a new routing protocol for WSNs that has less control packets due to its on-demand (reactive) nature is proposed. This new routing protocol termed Termite-hill is borrowed from the principles behind the termite’s mode of communication. We first study the foraging principles of a termite colony and utilize the inspirational concepts to develop a distributed, simple and energy-efficient routing protocol for WSNs. We perform simulation studies to compare the behavior and performance of the Termite-hill design with an existing classical and on-demand protocol (AODV) and other Swarm Intelligence (SI) based WSN protocols in both static, dynamic and mobility scenarios of WSN. The simulation results demonstrate that Termite-hill outperforms its competitors in most of the assumed scenarios and metrics with less latency. Further studies show that the current practice in modeling and simulation of wireless sensor network (WSN) environments has been towards the development of functional WSN systems for event gathering, and optimization of the necessary performance metrics using heuristics and intuition. The evaluation and validation are mostly done using simulation approaches and practical implementations. Simulation studies, despite their wide use and merits of network systems and algorithm validation, have some drawbacks like long simulation times, and practical implementation might be cost ineffective if the system is not properly studied before the design. We therefore argue that simulation based validation and practical implementation of WSN systems and environments should be further strengthened through mathematical analysis. To conclude this work and to gain more insight on the behavior of the termite-hill routing algorithm, we developed our modeling framework for WSN topology and information extraction in a grid based and line based randomly distributed sensor network. We strengthen the work with a model of the effect of node mobility on energy consumption of Termite-hill routing algorithm as a function of event success rate and occasional change in topology. The results of our mathematical analysis were also compared with the simulation results.
... Literature has studied the main techniques of saving-energy (Jiang et al. 2007;Khan, Qureshi, and Iqbal 2015). DVS has been discussed in Sausen et al. (2008) and interplay of both DPM and DVS has been considered. ...
Cyber-Physical Production Systems (CPPS) often use wireless sensor networks (WSNs) for monitoring purposes. However, data from WSNs may be inaccurate and unreliable due to power exhaustion, noise and other issues. In order to achieve a reliable and accurate data acquisition while ensuring low energy consumption and long lifetime of WSNs, data cleansing algorithms for energy-saving are proposed in this research. The cleansing algorithms are computationally lightweight in local sensors and energy-efficient due to low energy consumption in communications. Dynamic voltage scaling and dynamic power management are adopted for reducing energy consumption, without compromising the performance at system level. A low-power protocol for sink node communication is proposed at network level. A health monitoring system for a Cyber-Physical Machine Tool (a typical example of CPPS) is designed. Experiment results show that the proposed energy-saving data cleansing algorithm yields high-performance and effective monitoring.
... A sensor which is currently sensing and processing data is called in active state. Major energy consumer components of a sensor is shown in Fig. 10 and studied in [33][34][35]. Per bit energy consumption in transmitting E t with distance d can be expressed as ...
Wireless Sensor Networks (WSNs) has become one the promising research theme
due to the availability of wide range of applications useful for almost each and every area of
society. One fundamental application of WSNs is event detection in a Region of Interest (RoI).
A set of sensors are deployed to monitor any events inside RoI. In such monitoring applications,
both the quality of detection as well as resource requirement in terms of sensors must be
optimized while satisfying a certain level of detection guaranty. Therefore, carrying out an
optimal sensor deployment is a challenging task for achieving a certain coverage quality with
minimum energy consumption and network cost. This paper proposes analytical models for
critically analyzing the performance of different deployment techniques to provide insight on
the design parameters and system behaviors. Mathematical formulations have been derived to
measure the quality of coverage, energy consumption and network cost of geometrical
deployment patterns in terms of different metrics; namely, size of RoI, number of sensors and
their sensing range. The deployment patterns are modeled by using different shapes of
mathematical geometry such square, tri-tilling-hexagon and hexagon. Simulations are carried
out using MATLAB considering realistic parameter setting and results are comparatively
analyzed for deployment techniques. Analysis of results attests the superiority of Tri Hexagon
Tiling (THT) deployment in terms of 2-coverage, energy consumption and network cost to the
square and hexagon deployments. In terms of 3-coverage and 4-coverage, hexagon deployment
is better as compared to THT and square deployments.
The Information centric networking paradigm has proven particularly useful for the constrained Internet of Things (IoT), in which nodes are challenged by end-to-end communication without network assistance. This work focuses on the interaction between possibly mobile sensors and actuators in such IoT regimes which deploy the Named-Data Networking (NDN) architecture. Constrained nodes in interactive scenarios need to be highly responsive but can only manage limited control state. We argue that the request-driven NDN networking paradigm, which prevents pushing of unsolicited data, should be preserved to confine the attack surface, whereas unsolicited link-local signaling can accelerate responses without sacrificing security.
In this paper, we contribute HoP-and-Pull (HoPP), a robust publish–subscribe scheme for typical IoT scenarios that targets low-power and lossy wireless networks running hundreds of resource constrained devices at intermittent connectivity. Our approach limits in-memory forwarding state to a minimum and naturally supports producer mobility, a temporary partitioning of networks, data aggregation on intermediary hops, and near real-time reactivity. We thoroughly evaluate the protocol by experiments in a realistic, large testbed with varying numbers of constrained devices, each interconnected via IEEE 802.15.4 wireless LoWPANs. We compare HoPP with common ICN pub-sub and mobility schemes as well as with basic MIPv6 and anchor-based multicast mobility. Implementations are built on CCN-lite with RIOT and support experiments using various single- and multi-hop scenarios.
Energy-constrained sensor nodes can adaptively optimize their energy consumption if a continuous measurement is provided. This is of particular importance in scenarios of high dynamics such as with energy harvesting. Still, self-measuring of power consumption at reasonable cost and complexity is unavailable as a generic system service.
In this article, we present ECO, a hardware-software co-design that adds autonomous energy management capabilities to a large class of low-end IoT devices. ECO consists of a highly portable hardware shield built from inexpensive commodity components and software integrated into the RIOT operating system. RIOT supports more than 200 popular microcontrollers. Leveraging this flexibility, we assembled a variety of sensor nodes to evaluate key performance properties for different device classes. An overview and comparison with related work shows how ECO fills the gap of in situ power attribution transparently for consumers and how it improves over existing solutions. We also report about two different real-world field trials, which validate our solution for long-term production use.
Wireless sensor networks have the potential to revolutionize soil ecology by providing abundant data gathered at temporal and spatial granularities previously impossible. We will present the design and the results we gained by deploying multiple experimental networks for soil monitoring over the past three years. Our current deployments include turtle nest monitoring, soil temperature and moisture monitoring related to an urban CO2 flux tower and soil respiration measurements. The networks are now part of an end-to-end system.
We present T2, a second generation sensor network oper- ating system written in the nesC language. We describe why the limitations and problems of current OSes ne- cessitate a new design. T2 improves on current systems in three areas: platform support, application construc- tion, and reliability. We argue that existing systems ne- glected these properties in order to maximize flexibility. In contrast, T2 limits flexibility to that which applica- tions need, and leverages these constraints to improve the rest of the system. We evaluate T2 in comparison to TinyOS, and show how its structure simplifies applica- tions, makes porting to a new platform much easier, and improves system reliability. From these results, we dis- cuss the frictions present in component-based OSes and how T2's design and structure makes dealing with them more tractable.
Rapidly deployable sensor networks are portable, reusable, and can take advantage of a human user in the field attending to the deployment. Unfortunately, even small disruptions or problems in collected data must be addressed quickly, as the overall quantity of data gathered is small relative to longterm deployments. In this paper we describe a procedure for calibration and a system for online fault remediation. Care in the calibration process for ion selective electrodes used for water quality assists interpretation of the data. Scientists will have more confidence in the data obtained from a rapid deployment if in-field users can detect and compensate for problems as they occur. We have designed and implemented a tool for use in the field to detect potential faults and provide actions to remedy or validate the faulty data. In January of 2006 we deployed 48 sensors over a period of 12 days in Bangladesh in order to aid in validating a hypothesis on the mass presence of arsenic in the groundwater. Our system is based on the the approximately 25,000 measurements we collected. ABSTRACT Rapidly deployable sensor networks are portable, reusable, and can take advantage of a human user in the field attending to the deployment. Unfortunately, even small disruptions or problems in collected data must be addressed quickly, as the overall quantity of data gathered is small relative to long-term deployments. In this paper we describe a procedure for calibration and a system for online fault remediation. Care in the calibration process for ion selective electrodes used for water quality assists interpretation of the data. Scientists will have more confidence in the data obtained from a rapid deployment if in-field users can detect and compensate for problems as they occur. We have designed and implemented a tool for use in the field to detect potential faults and provide actions to remedy or validate the faulty data. In January of 2006 we deployed 48 sensors over a period of 12 days in Bangladesh in order to aid in validating a hypothesis on the mass presence of arsenic in the groundwater. Our system is based on the the approximately 25,000 measurements we collected.
We present TinyOS, a flexible, application-specific operating system for sensor networks. Sen-sor networks consist of (potentially) thousands of tiny, low-power nodes, each of which ex-ecute concurrent, reactive programs that must operate with severe memory and power con-straints. The sensor network challenges of limited resources, event-centric concurrent appli-cations, and low-power operation drive the design of TinyOS. Our solution combines flexible, fine-grain components with an execution model that supports complex yet safe concurrent op-erations. TinyOS meets these challenges well and has become the platform of choice for sensor network research; it is in use by over a hundred groups worldwide, and supports a broad range of applications and research topics. We provide a qualitative and quantitative evaluation of the system, showing that it supports complex, concurrent programs with very low memory requirements (many applications fit within 16KB of memory, and the core OS is 400 bytes) and efficient, low-power operation. We present our experiences with TinyOS as a platform for sensor network innovation and applications.
The use of environmental energy is now emerging as a feasible energy source for embedded and wireless computing systems such as sensor networks where manual recharging or replacement of batteries is not practical. However, energy supply from environmental sources is highly variable with time. Further, for a distributed system, the energy available at its various locations will be different. These variations strongly influence the way in which environmental energy is used. We present a harvesting theory for determining performance in such systems. First we present a model for characterizing environmental sources. Second, we state and prove two harvesting theorems that help determine the sustainable performance level from a particular source. This theory leads to practical techniques for scheduling processes in energy harvesting systems. Third, we present our implementation of a real embedded system that runs on solar energy and uses our harvesting techniques. The system adjusts its performance level in response to available resources. Fourth, we propose a localized algorithm for increasing the performance of a distributed system by adapting the process scheduling to the spatio-temporal characteristics of the environmental energy in the distributed system. While our theoretical intuition is based on certain abstractions, all the scheduling methods we present are motivated solely from the experimental behavior and resource constraints of practical sensor networking systems.
We present novel approaches to managing three key resources in an event-driven sensornet OS: memory, energy, and peripherals. We describe the factors that necessitate using these new approaches rather than existing ones. A combination of static allocation and compile-time virtualization isolates resources from one another, while dynamic management provides the flexibility and sharing needed to minimize worst-case overheads. We evaluate the effectiveness and efficiency of these management policies in comparison to those of TinyOS 1.x, SOS, MOS, and Contiki. We show that by making memory, energy, and peripherals first-class abstractions, an OS can quickly, efficiently, and accurately adjust itself to the lowest possible power state, enable high performance applications when active, prevent memory corruption with little RAM overhead, and be flexible enough to support a broad range of devices and uses.
SP has been proposed as a unifying abstrac-tion or "narrow waist" for the SensorNet Architecture. An early prototype demonstrated the ability to allow multiple network protocols to co-exist and mutually benefit by sharing common resources without the drawback of larger program size or lesser performance. However, the early implementation failed to address the policy of resource sharing between network protocols, did not permit co-existing link layers, was evaluated on a limited scope of controlled experiments, and lacked the capability to evolve with evaluation, hindering broad adoption by the sensornet community. We provide a concrete and stable system that fully enables resource sharing among multiple concurrent network and link protocols, is easily portable to existing protocols and flexible to accommodate future protocols, and modularizes the policy engine of SP to enrich the control available to system designers, all while remaining competitive in the size of program and memory footprints. By enabling the community to evaluate and explore pro-tocol interoperability, SP promotes code factorization and reuse, facilitating development of protocols and systems with expanded capabilities, enhanced performance, and greater energy-efficiency.