Energy Overhead Research Articles

On the Energy Footprint of Mobile Testing Frameworks

High energy consumption is a challenging issue that an ever increasing number of mobile applications face today. However, energy consumption is being tested in an <i>ad hoc</i> way, despite being an important non-functional requirement of an application. Such limitation becomes particularly disconcerting during software testing: on the one hand, developers do not really know how to measure energy; on the other hand, there is no knowledge as to what is the energy overhead imposed by the testing framework. In this paper, as we evaluate eight popular mobile UI automation frameworks, we have discovered that there are automation frameworks that increase energy consumption up to roughly 2200 percent. While limited in the interactions one can do, <i>Espresso</i> is the most energy efficient framework. However, depending on the needs of the tester, <i>Appium</i>, <i>Monkeyrunner</i>, or <i>UIAutomator</i> are good alternatives. In practice, results show that deciding which is the most suitable framework is vital. We provide a decision tree to help developers make an educated decision on which framework suits best their testing needs.

A Novel Hybrid Cache Coherence with Global Snooping for Many-core Architectures

Cache coherence ensures correctness of cached data in multi-core processors. Traditional implementations of existing protocols make them unscalable for many core architectures. While snoopy coherence requires unscalable ordered networks, directory coherence is weighed down by high area and energy overheads. In this work, we propose Wireless-enabled Share-aware Hybrid (WiSH) to provide scalable coherence in many core processors. WiSH implements a novel Snoopy over Directory protocol using on-chip wireless links and hierarchical, clustered Network-on-Chip to achieve low-overhead and highly efficient coherence. A local directory protocol maintains coherence within a cluster of cores, while coherence among such clusters is achieved through global snoopy protocol. The ordered network for global snooping is provided through low-latency and low-energy broadcast wireless links. The overheads are further reduced through share-aware cache segmentation to eliminate coherence for private blocks. Evaluations show that WiSH reduces traffic by and runtime by , while requiring smaller storage and lower energy as compared to existing hierarchical and hybrid coherence protocols. Owing to its modularity, WiSH provides highly efficient and scalable coherence for many core processors.

SecNVM: Power Side-Channel Elimination Using On-Chip Capacitors for Highly Secure Emerging NVM

Emerging nonvolatile memories (NVMs), such as resistive RAM (RRAM) and spin-transfer-torque RAM (STTRAM), present exciting opportunities for data storage applications and offer improved access speeds, retention times, power consumption, and scalability. However, these technologies leak the Hamming weight of data through power side-channel during read and write operations. We propose a technique leveraging on-chip capacitor and voltage regulator (VR) that powers the NVM read/write operations. The side-channel leakage is eliminated due to the isolation of memory array from the external power supply during read/write operations. The residual charge on capacitor bank is discarded safely to prevent information leakage during capacitor recharging. The VR ensures a steady voltage during the entire read/write operations even though the capacitor discharges. The design presents a performance (instructions per cycle) degradation of 0.53%-1.2% under parsec and splash-2 benchmarks and incurs an area overhead of ~ 3.54×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> % and an energy overhead of ~ 3.05 ×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> % for a 4-Mb RRAM memory array. For a 64-bit word, the design improves security by 2.7 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">19</sup> × to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">64</sup> ×. SecNVM should be used in small security-critical memory macros to limit the overhead. SecNVM is generic and could protect any security module such as encryption engines, against power side-channel attacks.

An Energy-Efficient Conditional Biasing Write Assist With Built-In Time-Based Write-Margin-Tracking for Low-Voltage SRAM

Write assists (WAs), such as negative bitline and collapse supply voltage (VDD), can effectively improve the write V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> of static random access memory (SRAM) cells. The energy overhead associated with such assists is considerable due to the switching activities on high capacitive nodes for every write operation. In this brief, a conditional biasing WA with built-in time-based write-margin-tracking is proposed to avoid unnecessary assist for energy saving. The biasing voltage for assisting the write procedure is only adjusted during write failures and remains unchanged for native write-success cells. Compared with conventional WAs, the proposed design can reduce write energy by 29%-34% with a similar area overhead. In addition, silicon measurements have demonstrated that the proposed assist is functional for near-threshold operations and V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DDmin</sub> is reduced to 0.4 V.

Energy‐efficient remote mobile device management in infrastructure‐less environment for emergency assessment

SummaryPersonal mobile devices, or PMDs, have the main objective of supporting connectivity in infrastructured network. However, they are rarely researched to support connectivity in infrastructure‐less network environment, particularly in case of no network or when existing network is damaged. This research direction has significant benefit towards emergency management, where PMDs in affected areas can be configured to establish inexpensive infrastructure‐less network for victims' information gathering. Existing PMD configurations techniques either lack flexibility and hinder device reachability or are slow and consume significant energy. These factors have motivated towards the two‐fold contribution proposed in this paper—(1) a novel over‐the‐air mobile device management approach of PMDs to establish multi‐hop infrastructure‐less network and (2) additional over‐the‐air sensing configuration of PMDs to record and transmit incapacitated victims' surrounding conditions using proposed network. These contributions allow multiple PMDs to establish network with minimal time and energy overheads, leading to efficient emergency management. Simulation and testbed results validate proposed approaches.

Towards generating a reliable device-specific identifier for IoT devices

A significant number of IoT devices are being deployed in the wild, mostly in remote locations and in untrusted conditions. This could include monitoring an electronic perimeter fence or a critical infrastructure such as telecom and power grids. Such applications rely on the fidelity of data reported from the IoT devices, and hence it is imperative to identify the trustworthiness of the remote device before taking decisions. Existing approaches use a secret key usually stored in volatile or non-volatile memory for creating an encrypted digital signature. However, these techniques are vulnerable to malicious attacks and have significant computation and energy overhead. This paper presents a novel device-specific identifier, IoT-ID that captures the device characteristics and can be used towards device identification. IoT-ID is based on physically unclonable functions (PUFs), that exploit variations in the manufacturing process to derive a unique fingerprint for integrated circuits. In this work, we design novel PUFs for Commercially Off the Shelf (COTS) components such as clock oscillators and ADC, to derive IoT-ID for a device. Hitherto, system component PUFs are invasive and rely on additional dedicated hardware circuitry to create a unique fingerprint. A highlight of our PUFs is doing away with special hardware. IoT-ID is non-invasive and can be invoked using simple software APIs running on COTS components. IoT-ID has the following key properties viz., constructability, real-time, uniqueness, and reproducibility, making them robust device-specific identifiers.We present detailed experimental results from our live deployment of 50 IoT devices running over a month. Our edge machine learning algorithm has 100% accuracy in uniquely identifying the 50 devices in our deployment and can run locally on the resource-constrained IoT device. We show the scalability of IoT-ID with the help of numerical analysis on 1000s of IoT devices. Further, we discuss approaches to evaluate and improve the reliability of the IoT-ID.11This manuscript is an extension of the paper ‘IoT-ID: A Novel Device-Specific Identifier Based on Unique Hardware Fingerprints’ Vaidya et al. (2020) published in 2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI).

Energy-efficient collection of wearable sensor data through predictive sampling

Wearable sensors have emerged as viable and attractive solutions for monitoring the health of people under risk of major problems such as hypertension, heart attacks, and athletes overstressing their bodies. These devices would report on the status of certain body organs to a gateway node over wireless links. A major challenge for effective use of these miniaturized devices is sustaining their operation using a limited energy supply. Therefore, minimizing energy consumption is a crucial design goal. In this paper we propose a novel approach for reducing the volume and frequency of data transmission through data sample prediction. Our methodology is based on applying advanced machine learning techniques to determine when data transmissions are skipped, and by implicitly making the gateway aware of the omitted samples in order to achieve accurate signal reconstruction. The paper also presents a data quantization technique for increased throughput and reduced energy overhead while sustaining desired medical assessment accuracy. Furthermore, a packet formation algorithm is proposed to leverage the available buffering space to improve bandwidth and energy utilization subject to latency constraints. The effectiveness of our approach is validated using publicly available Electrocardiography and Electromyography datasets and is shown not only to outperform conventional data compression methods but can also be applied in conjunction with them.

Adaptive time-bound access control for internet of things in fog computing architecture

This paper proposes an adaptive Time-Bound Attribute-Based Encryption Scheme (TB-ABE). TB-ABE is a fine-grained data access control scheme that maintains a secure data exchange environment between IoT devices. The lifetime of the system is divided into equal periods of time slots with unique encryption keys. TB-ABE combines Cyphertext-Policy Attribute Based Encryption with Time-bound keys. It only permits data users with attributes that satisfy the access policy set by the data owner to access the data for a specific period. It also proposes an efficient indirect revocation handling mechanism with minimal computations. The proposed scheme is implemented in Fog-Cloud computing architecture, where fog devices are used for partial outsourcing of encryption and decryption operations. This paper provides security and performance analysis for the proposed scheme in terms of computation, storage, communication and energy overheads. Analysis of proposed scheme proves its feasibility for limited-resource devices, while maintaining a secure communication between IoT devices.

An Algorithm with Efficiently Collecting and Aggregating Data for Wireless Sensor Networks

Due to the resource constraint, in wireless sensor network, the node processing ability, wireless bandwidth and battery capacity and other resources are scarcer. For improving the energy efficient and extend the lifetime of the network, this paper proposes a novel algorithm with the distributed and energy-efficient for collecting and aggregating data of wireless sensor network. In the proposed protocol, nodes can autonomously compete for the cluster head based on its own residual energy and the signal strength of its neighbouring nodes. To reduce the energy overhead of cluster head nodes, with a multi-hop way among cluster heads, the collected data from cluster heads is sent to a designated cluster head so as to further send these data to a base station. For improving the performance of the proposed protocol, a new cluster coverage method is proposed to fit the proposed protocol so that when the node density increases, network lifetime can be increased linearly as the number of nodes is increased. Simulations experiments show that network lifetime adopting the proposed protocol is sharply increased. And, the proposed protocol makes all the nodes die (network lifetime is defined as the death of last one node) in the last 40 rounds so that networks adopting the proposed protocol have higher reliability than networks adopting compared protocols.

Simba

Package-level integration using multi-chip-modules (MCMs) is a promising approach for building large-scale systems. Compared to a large monolithic die, an MCM combines many smaller chiplets into a larger system, substantially reducing fabrication and design costs. Current MCMs typically only contain a handful of coarse-grained large chiplets due to the high area, performance, and energy overheads associated with inter-chiplet communication. This work investigates and quantifies the costs and benefits of using MCMs with finegrained chiplets for deep learning inference, an application domain with large compute and on-chip storage requirements. To evaluate the approach, we architected, implemented, fabricated, and tested Simba, a 36-chiplet prototype MCM system for deep-learning inference. Each chiplet achieves 4 TOPS peak performance, and the 36-chiplet MCM package achieves up to 128 TOPS and up to 6.1 TOPS/W. The MCM is configurable to support a flexible mapping of DNN layers to the distributed compute and storage units. To mitigate inter-chiplet communication overheads, we introduce three tiling optimizations that improve data locality. These optimizations achieve up to 16% speedup compared to the baseline layer mapping. Our evaluation shows that Simba can process 1988 images/s running ResNet-50 with a batch size of one, delivering an inference latency of 0.50 ms.

Resource Scheduling Based on Priority Ladders for Multiple Performance Requirements in Wireless Body Area Networks

Media access control (MAC) plays a pivotal role in ensuring proper operation in wireless body area networks (WBANs). However, current solutions still cannot satisfy multiple performance requirements of such networks. In this article, we propose a priority ladders-based resource scheduling (PLRS) scheme to meet these demands. First, we design a multiple-level priority division approach, which is based on health parameters and data types of patients. Unlike only two priorities of nodes in other schemes, there are four priorities in our solution. This is more practical and more fair for different nodes. Moreover, we devise a ladders-based time-slot allocation method, in which time slots of nodes are increased round by round based on the priority ladders of nodes. This reduces the difference of the number of time slots between disparate nodes with close priorities, and accordingly enhances the fairness of nodes. In addition, we design an interval-based task ordering approach, in which nodes use the allocated time slots in dispersed multiple intervals rather than a continuous time span in conventional solutions. This guarantees high throughput of high-priority nodes and short waiting time of emergency data. Extensive simulations exhibit the advantages of PLRS in terms of energy overhead, delay, and utility.

Utility driven cooperative spectrum sensing scheduling for heterogeneous multi-channel cognitive radio networks

Owing to the spectrum scarcity and energy constrained devices in wireless networks arises the demand for an efficient spectrum sensing technique which improves both sensing performance and energy efficiency for cognitive radio networks. This paper proposes a cooperative spectrum sensing scheduling (CSSS) scheme for heterogeneous multi-channel cognitive radio networks with the objective of finding an efficient sensing schedule to enhance network utility while keeping the energy depletion at a lower level. We start with formulating the CSSS problem as an optimization problem, which captures both the energy-performance and performance opportunity trade-offs. We prove that the formulated CSSS problem is non-deterministic polynomial hard (NP-hard). To tackle the higher computational complexity of the formulated problem, we propose a greedy-based heuristic solution, which produces a sub-optimal result in polynomial time. To reduce energy consumption during spectrum sensing, we make secondary users to adaptively decide on the sensing duration based on the received signal-to-noise ratio (SNR), where higher SNR leads to lower sensing duration and vice-versa. For enhancing network throughput, SUs sense multiple channels in the order of their suitability for data transmission to explore as many numbers of channels as possible within the permitted maximum sensing time. We consider erroneous nature of reporting channel to make the cooperative decision robust against errors during reporting. Simulation based results show the effectiveness of the proposed scheme in terms of utility, energy overhead, and the number of channels explored compared to similar schemes from literature.

Minimizing the Lateness, Makespan, And Energy Efficient Factors in Job Shop Scheduling

Half of the worldwide energy is consumed by industries. In the modern era, due to an increase in the energy overhead, it is necessary to plan during electricity production. In this paper, a novel mathematical model is proposed which determine a more efficient scheduling algorithm that reduces the electricity consumption cost (ECC) and make-span for the Flexible Job Shop Scheduling Problem (FJSSP) with a time-of-use (TOU) policy. Another subtask named speed selection, which indicates the selection of varying operation speed is added to already available two subtasks of FJSSP. Subsequently, a Modified Cooperative Co-evolutionary Cuckoo Search Algorithm (MCCCSA) along with Variable Neighborhood Search (VNS) is used for resolving the constrained, unconstrained optimization and engineering issues. The population used for this algorithm is an organization that contains dynamic persons. Experimental analysis is done to check the performance of the proposed model MCCCSA+VNS for improved scheduling result when evaluated with other existing methods.

ICEr: An Intermittent Computing Environment Based on a Run-Time Module for Energy-Harvesting IoT Devices with NVRAM

With the development of energy-harvesting technology, various applications have been developed that can be operated only with harvested energy, thereby making energy-harvesting technology suitable for edge devices in poor environments where battery replacement is difficult. However, devices with energy-harvesting technology have limitations: an application can operate intermittently in an energy-harvesting device, and the device’s energy is greatly affected by the environment and the state of the device. Intermittent computing causes abnormal progress or affords incorrect results. The factors affecting the energy of the device can change the operation of the device. To solve these problems, we propose the “Intermittent Computing Environment based on a run-time module” (ICEr), which dynamically controls and manages an application for normal operation in intermittent computing. ICEr comprises an energy checker and a controller. The energy checker measures the energy state of a device at run-time, and the controller controls and manages an application through Backup, Restore, Sleep, and Wakeup. The controller optimizes those operations by considering the energy state and memory state together to minimize time and energy overhead. In this study, two kinds of experiments were conducted. In the first experiment, Embench was selected as the target application to validate ICEr and measure its performance. This experiment validated that ICEr behaves dynamically in various environments. Moreover, it showed a reduction in relative execution time overhead of up to 50% and a reduction in energy overhead of up to 49.5% against Hibernus, depending on the environment. In the second experiment, ICEr was applied to the Temperature Measurement Application, and the improvement of the energy efficiency for the real Internet-of-Things (IoT) application was confirmed.

Amnesiac DRAM: A Proactive Defense Mechanism Against Cold Boot Attacks

DRAMs in modern computers or hand-held devices store private or often security-sensitive data. Unfortunately, one known attack vector, called a cold boot attack, remains threatening and easy-to-exploit, especially when attackers have physical access to the device. It exploits the fundamental property of current DRAMs: remanence effects that retain the stored contents for a certain period of time even after powering off. To magnify the remanence effect, cold boot attacks typically freeze the victim DRAM, thereby providing a chance to detach, move, and reattach it to an attacker's computer. Once power is on, attackers can steal all the security-critical information from the victim's DRAM, such as a master decryption key for an encrypted disk storage. Two types of defenses were proposed in the past: 1) CPU-bound cryptography, where keys are stored in CPU registers and caches instead of in DRAMs, and 2) full or partial memory encryption, where sensitive data are stored encrypted. However, both methods impose non-negligible performance or energy overheads to the running systems, and worse, significantly increase the hardware and software manufacturing costs. We found that these proposed solutions attempted to address the cold boot attacks passively: either by avoiding or by indirectly addressing the root cause of the problem, the remanence effect. In this article, we propose and evaluate a proactive defense mechanism, Amnesiac DRAM, that comprehensively prevents the cold boot attacks. The key idea is to discard the contents in the DRAM when attackers attempt to retrieve (i.e., power on) them from the stolen DRAM. When Amnesiac DRAM senses a physical separation, it locks itself and deletes all the remaining contents, making it amnesiac. The Amnesiac DRAM causes neither performance nor energy overhead in ordinary operations (e.g., load and store) and can be easily implemented with negligible area overhead in commodity DRAM architectures.

Deep Pipeline Circuit for Low-Power Spintronic Devices

As the traditional CMOS technology encounters significant scaling challenges, many emerging beyond-CMOS devices have been proposed and developed to augment or even replace CMOS devices. Spintronic devices have received extensive attention due to several unique attributes, including a low operation voltage and nonvolatility. However, spintronic devices are intrinsically slow because of the long switching delay of the magnet. To overcome the drawback, in this article, we propose and develop a deep pipeline method for generic spintronic majority-gate-based circuits. A fast and efficient pipeline buffer insertion methodology is integrated into the industry-standard placement and routing design flow to ensure the correct functionality. The proposed framework can accurately capture 1) the timing of a multiphase clock-gated spintronic circuit and 2) the energy overhead associated with the supply clocking, which is one major overhead of spintronic circuits. Based on the proposed framework, we demonstrate that a spintronic circuit implemented with a deep pipeline can provide over $10\times $ reduction in energy-delay products compared to their traditional CMOS-based counterparts. In the case study, the proposed deep pipeline method is applied to three emerging spintronic devices. Results show that device-level parameters have significant impacts on circuit-level performance and optimal logic depth.

Optimized Fuzzy Commitment Based Key Agreement Protocol for Wireless Body Area Network

Wireless body area networks composed of wearable devices have increasingly become an integral part of personal health monitoring and medical assistance. A series of key agreement schemes using heartbeats as a source of trust have been proposed to protect health data transferred among devices carried by a person from leakage. However, because of the unrealistic assumption in their threat model and the use of fixed parameters, previous schemes have unstable performance for different individuals and even have the risk of security degradation such that the schemes can be compromised by attackers. In this paper, we present an optimal key agreement scheme based on heartbeat, to ensure secure communication between legitimate devices. The proposed key agreement scheme employs fuzzy commitment with low latency in feature extraction. Moreover, the physiological-distribution-based parameter optimization (PDPO) algorithm is proposed to adaptively determine the optimal protocol parameters for individuals, which not only ensures outstanding and stable performance but also guarantee the security of the scheme. Finally, we prototype our protocol and conduct experiments with multiple subjects to evaluate its security and performance. Our results demonstrate that the proposed protocol negotiates the key securely and quickly, has low energy overhead, and is suitable for practical applications.

Energy‐efficient multi‐biometric system for Internet of Things using trust management

The traditional password-based authentication systems are easy to breach because they rely on what the user knows and not on who is the user. This makes them prone to impersonation, as the culprit can gain access to the system once they knew the user's password, which led to the development of biometric-based authentication systems. Nonetheless, noisy data, intra-, and inter-class variations render these systems inaccurate. However, combining multiple biometric traits (multi-biometric systems) promises to increase the accuracy of biometric systems. But, this leads to an overhead in energy and latency in the system. These overheads are frowned at in embedded systems, especially those deployed in remote areas. Here, the authors propose a trust management-based multi-biometric system. The system switches between a uni-biometric and a multi-biometric system to achieve high accuracy and reduced overhead (i.e. energy and latency). The system uses the uni-biometric technique for trusted users and the multi-biometric method for untrustworthy users who are zero-effort impostors. It is found to reduce the false positive rate by 0.29 of Fingerprint uni-biometric system and the false negative rate by a factor of 0.5 of the multi-biometric system. Also, the energy consumption is reduced by a factor of 0.2 of the multi-biometric system.

Trade-off between spectral efficiency and normalized energy in Ad-hoc wireless networks

With the ever-increasing deployment of wireless communication technologies, effective management of scarce power and radio spectrum resources is of primary concern. The available bandwidth spectrum should be utilized in the most efficient and controlled way for practical economic growth of industries in conformity with the evolving demand of energy for powering wireless devices. In the proposed optimization scheme, energy management and limited spectrum sharing are distributively implemented for handling quality of service provisioning and potential resource allocation across multiple nodes with inherent operational capability deficiencies. We employ cross-layer information exchange and convex optimization techniques to simultaneously achieve more efficient radio spectrum usage and optimal energy consumption in ad-hoc wireless networks with distributed scenarios. We consider the time-invariant additive white Gaussian noise channel and the time-varying Rayleigh fading channel to study the trade-off between the two network design objectives of achieving improved spectrum efficiency and minimizing the energy overhead in data routing paradigm. The original problem is transformed into an equivalent convex optimization problem through logarithmic processing to obtain the approximate global optimal solution. Moreover, the robustness and efficiency of the proposed framework is evaluated in large-scale set ups to provide the scalability analysis on both the employed performance objectives. The developed optimization scheme is compared with the machine learning models previously proposed in literature by employing the computational and/or time complexity metrics. Finally, the effectiveness of the proposed optimization model is substantiated through the simulation comparison results with the existing schemes in terms of various key performance parameters such as throughput, energy efficiency, and average bit errors.

Delay-Aware Green Routing for Mobile-Sink-Based Wireless Sensor Networks

Mobile sinks were introduced in wireless sensor networks (WSNs) to mitigate the infamous hotspot problem. However, routing in mobile-sink-based WSNs requires frequent updation of sink location information to all the sensor nodes; which is an energy-expensive process for resource-constrained WSNs. Therefore, it is required to develop a green routing protocol that can minimize the energy overhead in sink location updation as well as reduce the data delivery delay. This article proposes a virtual-infrastructure-based delay-aware green routing protocol (DGRP) that creates multiple rings in the sensor field and limits the updation of mobile sink location information to the nodes belonging to the rings only. Simulation results show that DGRP outperforms existing routing protocols in terms of energy consumption and throughput. In addition to this, DGRP results in $\approx 26$ %, $\approx 39$ %, and $\approx 35$ % improvement in data delivery delay for a varying number of sensor nodes, sink speeds, and network sizes, respectively, when compared with the state of the art.