Energy-delay Tradeoff Research Articles

Security and performance-aware resource allocation for enterprise multimedia in mobile edge computing

Mobile edge computing (MEC) is a promising computing model and has gained remarkable popularity, as it deploys the resources (e.g., computation, network, and storage) to the evolved NodeB (eNB) to provide enormous benefits such as low delay and energy consumption. More and more enterprises construct their edge computing platforms to store multimedia contents (i.e., video, audio, photos, and text data) for the user equipment (UE). However, both the eNB and UEs will experience serious security attacks when transmitting or receiving multimedia data via the wireless network. Existing MEC studies mainly focus on task offloading and performance improvement without considering the enterprise multimedia security problem. This paper proposes a security and performance-aware resource allocation (Spara) algorithm for enterprise multimedia in MEC environment. More specifically, we first build the architecture of enterprise multimedia security for sending the data requests to UEs, which mainly consists of computing and bandwidth resource allocation. Then, we formulate the stochastic data transmission problem to minimize the delay and energy consumption of UEs subject to the security guarantee. To achieve this goal, two queues, namely front-end queue and back-end queue, are used for each UE, and the Lyapunov optimization technique is applied to determine how to allocate the computing and bandwidth resources. Rigorous theoretical analysis shows that Spara algorithm meets the [O(1/V), O(V)] energy-delay tradeoff. Extensive simulation experiments validate this analysis result and the effectiveness of Spara algorithm.

Joint caching and sleeping optimisation for D2D‐aided ultra‐dense network

Device-to-device (D2D) communication provides the communication of the users in the vicinity and thereby decreases end-to-end delay and power consumption. More importantly, D2D communication enables offloading the traffic load of the base station (BS), and it is very suitable for caching, especially in ultra dense networks (UDNs). In this paper, a joint optimisation problem of collaborative caching and sleeping strategy based on energy-delay tradeoff for D2D-aided UDN is investigated. To solve the joint optimization problem, we decompose it into sleeping sub-problem and collaborative caching sub-problem. For sleeping subproblem, the optimal sleeping ratio is first derived, then, a delay-aware sleeping strategy is proposed to obtain the local optimal sleeping scheme. For the collaborative caching subproblem, the suboptimal solution is obtained by distributed iterative method. In each iteration step, the suboptimal solution is derived by solving the combinatorial optimisation problem under Karush-Kuhn-Tucker conditions. Simulation results show that the proposed algorithms can coverage to the global solution. It also demonstrate that increasing caching capacity shortened mean delay, and benefitting from collaborative caching, delay performance and energy saving can be improved significantly. Moreover, it can be seen that combining the sleeping strategy with collaborative caching further reduced energy consumption by a considerable amount.

Energy Efficiency and Delay Tradeoff for Wireless Powered Mobile-Edge Computing Systems With Multi-Access Schemes

The integration of Mobile-edge Computing (MEC) and Wireless Energy Transfer (WET) has been recognized as a promising technique to enhance computation capability and to prolong battery lifetime of resource-constrained wireless devices in the Internet of Things (IoT) era. However, it is challenging to jointly schedule energy, radio, and computational resources for coordinating heterogeneous performance requirements in wireless powered MEC systems. To fill this gap, this paper investigates the fundamental tradeoff between Energy Efficiency (EE) and delay in a multi-user wireless powered MEC system. Considering the random channel conditions and task arrivals, we formulate a stochastic optimization problem to study the EE-delay tradeoff, which optimizes network EE subject to network stability, maximum central processing unit frequency, peak transmission power, available communication resource, and energy causality constraints. Further, we propose the online computation offloading and resource allocation algorithm by transforming the original problem into a series of deterministic optimization problems in each time block based on Lyapunov optimization theory. In addition, theoretical analysis shows that the algorithm achieves the EE-delay tradeoff as [ ${O}(1/{V}), {O}({V})$ ] and introduces a control parameter ${V}$ to balance the EE-delay performance. Numerical results verify the theoretical analysis and reveal the impact of various parameters to the system performance.

On the Energy-Delay Tradeoff in Streaming Data: Finite Blocklength Analysis

This paper investigates basic trade-offs between energy and delay in wireless communication systems using finite blocklength theory. We first assume that data arrive in constant stream of bits, which are put into packets and transmitted over a communications link. Our results show that depending on exactly how energy is measured, in general energy depends √d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> or √Vd <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> log d, where d is the delay. This means on that the energy decreases quite slowly with increasing delay. Furthermore, to approach the absolute minimum of -1.59 dB on energy, bandwidth has to increase very rapidly, much more than what is predicted by infinite blocklength theory. We then consider the scenario when data arrive stochastically in packets and can be queued. We devise a scheduling algorithm based on finite blocklength theory and develop bounds for the energy-delay performance. Our results again show that the energy decreases quite slowly with increasing delay.

An Energy-Delay Trade-Off in Wireless Visual Sensor Networks Based on Two-Sided Matching

Wireless visual sensor networks (WVSNs) comprise a large number of battery-powered visual sensors with limited processing and transmission capacity in the field of interest. Hence, energy conservation is an important issue in resource-constrained WVSNs. In-network data fusion, clustering networks has been proved to be an effective scheme in reducing energy consumption. However, in most clustering algorithms, clustering may introduce bottlenecks to WVSNs which bring extra delay in the processes of data collection and forwarding. Such delay can be reduced by the design of routing schemes. In this paper, we propose an approach of energy-delay trade-off (EDT) based on two-sided matching theory. First, we determine a balance clustering structure which can balance energy consumption and delay in random-densely-deployed WVSNs. Then, the problem formulation of EDT problem in the balance clustering structure is provided. Moreover, the transformation strategy based on two-sided matching (TS-TSM) is proposed. After the TS-TSM, the EDT problem is transformed into a stable two-sided matching (STSM) problem with single objective function and the computational complexity is reduced. Furthermore, most of floating-point operations in original EDT problem are converted into integer operations which are easy to parallel compute. Finally, an improved deferred-acceptance algorithm (IDAA) is provided to generate a stable matching solution as the EDT scheme. Evaluation results demonstrate that the EDT approach can reduce delay in data aggregation processes and keep the total energy consumption at low levels. Further simulations show that the EDT approach is a fast approach, and it is not inferior to the existing algorithms.

MMedia: An Efficient Transmission Policy for Multimedia Applications using Mobile Cloud Computing

Background &amp; Objective: Multimedia aggregates various types of media such as audio, video, images, animations, etc., to form a rich media content which produces an everlasting effect in the minds of the people. Methods: In order to process multimedia applications using mobile devices, we encounter a big challenge as these devices have limited resources and power. To address these limitations, in this work, we have proposed an efficient approach named as mMedia, wherein multimedia applications will utilize the multi cloud environment using Mobile Cloud Computing (MCC), for faster processing. The proposed approach selects the best available network. The authors have also considered using the Lyapunov optimization technique for efficient transmission between the mobile device and the cloud. Results: The simulation results indicate that mMedia can be useful for various multimedia applications by considering the energy delay tradeoff decision. Conclusion: The results have been compared alongside the base algorithm SALSA on the basis of different parameters like time average queue backlog, delay and time average utility and indicate that the mMedia outperforms in all the aspects.

On the Queuing Model of the Energy-Delay Tradeoff in Wireless Links With Power Control and Link Adaptation

A transmission profile refers to a transmission power and a modulation and coding scheme to be used for packet transmissions over a wireless link. The goal of this paper is to develop transmission profile selection policies so as to minimize the average power consumption on a wireless link while satisfying a certain delay constraint given in terms of a delay violation probability. Toward the assessment of profile selection policies, a multi-regime Markov fluid queue model is proposed to obtain the average power consumption and the queue waiting time distribution which allows one to analyze the energy-delay tradeoff in queuing systems for which the packet transmission duration is allowed to depend on the delay experienced by the packet until the beginning of service. Numerical examples are presented with transmission profiles obtained from realistic LTE simulations. Several transmission profile selection policies are proposed and subsequently compared using the analytical model.

BS sleeping strategy for energy-delay tradeoff in wireless-backhauling UDN

Ultra-dense network (UDN) has been recognized as a promising technology for 5G. Although turning off low-load base stations (BSs) can improve energy efficiency, it may cause degradation of delay performance. This makes energy-delay tradeoff (EDT) an important topic. In this paper, a theoretical framework for EDT, in wireless-backhauling UDN, is developed. First, we investigate association probabilities of UEs and transmission probabilities of BSs. Expressions for energy consumption and network packet delay are obtained and the impact that BS sleeping ratio has on energy consumption and packet delay are analyzed. Then, we formulate the EDT problem as a cost minimization problem to select the optimal set of sleeping small cells. To solve the EDT optimization problem, a locally optimal sleeping ratio for EDT is obtained using the dynamic gradient iteration algorithm and we prove that it can converge to the global optimal sleeping ratio. Then, queue-aware and channel-queue-aware sleeping strategies are proposed to find the optimal set of sleeping small cells according to the optimal sleeping ratio. We then see that the simulation and numerical results confirm the effectiveness of the proposed sleeping schemes.

Energy Efficient V2X-Enabled Communications in Cellular Networks

Vehicle-to-everything (V2X) communications, which provide wireless connectivity among vehicles, roadside drivers, passengers, and pedestrians, are attracting great interest. In this paper, we propose an energy-efficient relay assisted transmission scheme based on V2X communications in the uplink cellular networks for delay insensitive applications. We aim to maximize energy efficiency of the uplink while considering both the circuit power and transmit power consumption, as well as the delay constraint. The optimal resource allocation for direct transmission mode and V2X-enabled transmission mode are derived. For the V2X-enabled transmission mode, we first consider the transmission without the delay constraint and obtain the corresponding optimal power allocation. Based on the obtained result, we further propose the optimal resource allocation policy that satisfies the delay constraint and minimizes the energy consumption. Simulation results show that there exists an energy-delay tradeoff and the proposed V2X-enabled scheme can achieve significant energy saving with an acceptable delay compared with the traditional direct scheme, the decoding-and-forward based transmission scheme, and the opportunistic store-carry and forward based transmission scheme, especially when the cell radius is not small.

Energy efficient and delay aware ternary-state transceivers for aerial base stations

In recent times, Aerial Base Stations(AeBSs) are being investigated to provide wireless coverage to terrestrial radio terminals. There are many advantages of using aerial platforms to provide wireless coverage, including larger coverage in remote areas and better line-of-sight conditions, etc. Energy is a scarce resource for the AeBSs, hence the wise management of energy is quite beneficial for the aerial network. In this context, we study the means of reducing the total energy consumption by designing and implementing an energy efficient AeBSs as presented in this paper. Implementing the sleep mode in the Base Stations (BSs) has been proven to be a very good approach for improving the energy efficiency and we propose a novel strategy for further improving energy efficiency by considering ternary state transceivers for AeBSs. Using the three state model, we propose a Markov Decision Process (MDP) based algorithm, which intelligently switches among three states of the transceivers based on the offered traffic meanwhile maintaining a prescribed minimum channel rate per user. We define a reward function for the MDP, which helps us to get an optimal policy for selecting a particular mode for the transceivers of the AeBS. Considering an AeBS with transceivers whose states are changeable, we perform simulations to analyse the performance of the algorithm. Our results show that, compared with the always active model, around 40% gain in the energy efficiency is achieved by using our proposed MDP algorithm together with the three-state transceivers model. We also show the energy-delay tradeoff in order to design an efficient AeBS.

Energy-Delay Tradeoff for Dynamic Offloading in Mobile-Edge Computing System With Energy Harvesting Devices

Mobile-edge computing (MEC) has aroused significant attention for its performance to accelerate application's operation and enrich user's experience. With the increasing development of green computing, energy harvesting (EH) is considered as an available technology to capture energy from circumambient environment to supply extra energy for mobile devices. In this paper, we propose an online dynamic tasks assignment scheduling to investigate the tradeoff between energy consumption and execution delay for an MEC system with EH capability. We formulate it into an average weighted sum of energy consumption and execution delay minimization problem of mobile device with the stability of buffer queues and battery level as constraints. Based on the Lyapunov optimization method, we obtain the optimal scheduling about the CPU-cycle frequencies of mobile device and transmit power for data transmission. Besides, the dynamic online tasks offloading strategy is developed to modify the data backlogs of queues. The performance analysis shows the stability of the battery energy level and the tradeoff between energy consumption and execution delay. Moreover, the MEC system with EH devices and task buffers implements the high energy efficient and low latency communications. The performance of the proposed online algorithm is validated with extensive trace-driven simulations.

Energy Efficient Dynamic Resource Optimization in NOMA System

Non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) is a promising technique for next generation wireless communications. Using NOMA, more than one user can access the same frequency-time resource simultaneously and multi-user signals can be separated successfully using SIC. In this paper, resource allocation algorithms for subchannel assignment and power allocation for a downlink NOMA network are investigated. Different from the existing works, here, energy efficient dynamic power allocation in NOMA networks is investigated. This problem is explored using the Lyapunov optimization method by considering the constraints on minimum user quality of service and the maximum transmit power limit. Based on the framework of Lyapunov optimization, the problem of energy efficient optimization can be broken down into three subproblems, two of which are linear and the rest can be solved by introducing a Lagrangian function. The mathematical analysis and simulation results confirm that the proposed scheme can achieve a significant utility performance gain and the energy efficiency and delay tradeoff is derived as [O(1/V), O(V)] with V as a control parameter under maintaining the queue stability.

Optimal Sleeping Mechanism for Multiple Servers With MMPP-Based Bursty Traffic Arrival

A fundamental problem in green communications and networking is the operation of servers (routers or base stations) with sleeping mechanism to optimize energy-delay tradeoffs. This problem is very challenging when considering realistic bursty (non-Poisson) traffic. We prove for the first time that the optimal structure of such a sleeping mechanism for multiple servers when the arrival of jobs is modeled by a bursty Markov-modulated Poisson process (MMPP). It is shown that the optimal operation, which determines the number of active (or sleeping) servers dynamically, is hysteretic and monotone, and hence, it is a queue-threshold-based policy. This letter settles a conjecture in the literature that the optimal sleeping mechanism for a single server with interrupted Poisson arrival process, which can be treated as a special case of MMPP, is queue-threshold-based. The exact thresholds are given by numerically solving the Markov decision process.

Greenput: A Power-Saving Algorithm That Achieves Maximum Throughput in Wireless Networks

The dynamic frame sizing algorithm is a throughput-optimal algorithm that can achieve maximum network throughput without the knowledge of arrival rates. Motivated by the need for energy-efficient communication in wireless networks, in this paper, we propose a new dynamic frame sizing algorithm, called the Greenput algorithm, that takes power allocation into account. In our Greenput algorithm, time is partitioned into frames, and the frame size of each frame is determined based on the backlogs presented at the beginning of a frame. To obtain a good delay-energy efficiency tradeoff, the key insight of our Greenput algorithm is to reduce transmit power to save energy when the backlogs are low so as not to incur too much packet delay. For this, we define a threshold parameter $T_{\max }$ (for the minimum time to empty the backlogs with maximum power allocation), and the Greenput algorithm enters the (mixed) power-saving mode when the backlogs are below the threshold. Using a large deviation bound, we prove that our Greenput algorithm is still throughput optimal. In addition to the stability result, we also perform a fluid approximation analysis for energy efficiency and average packet delay when $T_{\max }$ is very large. To show the delay-energy efficiency tradeoff, we conduct extensive computer simulations by using the Shannon formula as the channel model in a wireless network. Our simulation results show that both energy efficiency and average packet delay are quite close to their fluid approximations even when $T_{\max }$ is moderately large.

Protocol Sequences With Carrier Sensing for Wireless Sensor Networks

Protocol sequences are deterministic binary sequences of a common period, which enjoy some special Hamming cross-correlation property by design. In contrast to random or contention-based medium access control schemes, a protocol sequence-based scheme can serve to provide at least a certain number of contention-free packet transmissions within a bounded delay for each asynchronous user in a feedback-free multiple access system. However, all protocol sequence-based schemes in the literature require that all sequence entries are mapped to slots with the same time duration, which produces a relatively low channel utilization. To overcome this inefficiency that is undesirable in delay-constrained wireless sensor networks, building on the idea of combining sequence-based access and carrier sensing, this paper proposes a new protocol sequence-based scheme, called the PS-CS. We derive the theoretical average throughput, average access delay, worst-case delay, and average energy consumption of the PS-CS. It is shown that the PS-CS produces average throughput close to the optimal capacity of ${p}$ -persistent carrier sense multiple access (CSMA), and enjoys smaller access delay than the optimal ${p}$ -persistent CSMA. In addition, we study the energy-delay tradeoff, impact of carrier sensing fault and channel error, and how to modify the PS-CS to support real-time downlink for feedback control.

Energy-Delay Tradeoff in Ultra-Dense Networks Considering BS Sleeping and Cell Association

Ultra-dense network (UDN) is a promising technology for future wireless networks to meet the explosively increasing wireless traffic demands. Sleeping strategy that switches base station (BS) on/off to reduce the energy consumption of UDN is widely investigated. However, the energy saving of sleeping strategy may come at the cost of increased delay. Hence, energy-delay tradeoff (EDT) which means how much energy can be saved by trading off tolerable delay is an important topic. In this paper, we develop a theoretical framework for EDT considering BS sleeping and cell association in UDNs. Firstly, an M/G/1/N processor sharing vacation queueing is introduced to model small cell considering sleeping strategy and cell association. Mean delay and energy consumption are quantitatively studied based on the queueing model. The general expressions for mean delay and energy consumption are derived. It shows that sacrificing delay will not always bring about energy saving. Then, EDT problem is formulated as a cost minimization problem, and the optimal sleeping time and association radius are investigated to achieve the optimal tradeoff between energy consumption and delay. Since there is no closed-form expression for the optimal sleeping time for arbitrary number of users associating with one small cell, the optimal sleeping time for one and infinite number of users are derived. Simulation results demonstrate that the optimal sleeping time for infinite number of users is a reasonable approximation for arbitrary number when it is more than one.

Joint Design of Routing and Power Control Over Unreliable Links in Multi-Hop Wireless Networks With Energy-Delay Tradeoff

Energy consumption and delay in end-to-end transmission, two significant cost metrics for guiding the design of routing protocols, are often relevant and even conflicting with each other. Therefore, it naturally becomes important to identify the tradeoff between them. In this paper, we investigate the joint design of routing and power control over unreliable communication links in multi-hop wireless networks with energy-delay tradeoff. Two popular retransmission schemes, hop-by-hop (HBH) scheme and end-to-end (E2E) scheme, are adopted to achieve the reliable transmissions over unreliable links. We model the process of packet transmission in HBH or E2E scheme as a random walk and then calculate the expected energy consumption and the expected delay needed to forward a packet. An expected cost function is defined to capture the tradeoff between energy consumption and delay. Due to the correlation between cost function and transmit power assigned on each link, power control technique is necessary to be integrated into the minimum expected cost routing (MECR) for each retransmission scheme. We prove that the optimal power assignment for each link in a candidate path, which minimizes the expected cost for packet transmission, uniquely exists. Then, the MECR algorithms are proposed for both HBH scheme and E2E scheme to find the optimal routing paths. Simulations illustrate that our proposed protocols not only attain the tradeoff between energy consumption and delay, but also outperform the existing protocols in terms of energy-efficiency and delay.

Optimal energy-delay tradeoff for opportunistic spectrum access in cognitive radio networks

Cognitive radio (CR) has been considered as a promising technology to enhance spectrum efficiency via opportunistic transmission at link level. Basic CR features allow secondary users (SUs) to transmit only when the licensed channel is not occupied by primary users (PUs). However, waiting for an idle time slot may lead to large packet delays and high energy consumption. We further consider that the SU may decide, at any moment, to use another dedicated way of communication (4G) in order to transmit his packets. Thus, we consider an Opportunistic Spectrum Access (OSA) mechanism that takes into account packet delay and energy consumption. We formulate the OSA problem as a Partially Observable Markov Decision Process (POMDP) by explicitly considering the energy consumption as well as packets’ delay, which are often ignored in existing OSA solutions. Specifically, we consider a POMDP with an average reward criterion. We derive structural properties of the value function and we show the existence of optimal strategies in the class of the threshold strategies. For implementation purposes, we propose online learning mechanisms that estimate the PU activity and determine the appropriate threshold strategy on the fly. In particular, numerical illustrations validate our theoretical findings.

Toward Joint Compression–Transmission Optimization for Green Wearable Devices: An Energy-Delay Tradeoff

Small-size and light-weight, as the modern design concept for the emerging wearable devices, has become a trend. However, such trend puts physical limitations to the battery, and the resulting short battery lifetime becomes the bottleneck for most wearable devices today. In this paper, we aim to optimize the energy usage through data compression and transmission rate control. We propose a novel joint compression-transmission approach, which not only minimizes the energy consumption of both compression and transmission, but also maintains the corresponding data distortion and transmission delay within a certain tolerant level. By adopting the Lyapunov framework, we develop an online algorithm to minimize the one-slot drift-plus-penalty function. We conduct numerical analysis and experimental study for our proposed approach. The results show that the size of queuing buffer has the significant impact on the energy cost. Next, we verify a fundamental tradeoff between the energy expenditure and transmission delay, and derive the theoretical performance bounds. After that, we show that the energy cost is also determined by the wireless channel gain and the data compression ratio. Finally, compared to a strategy without compression, our approach can save up to 92% of energy.

Energy-delay tradeoff and optimal base station sleeping control in hyper-cellular networks

By decoupling control coverage and traffic coverage, dynamic base station (BS) sleeping becomes possible in hyper-cellular networks. Traffic coverage can be adjusted based on real-time demands, to enhance the energy efficiency of the networks. However, sleeping mechanisms may affect the networks delay performance. Thus, it is important to obtain analytical results on the influence of sleeping mechanisms on the systems energy efficiency and delay performance. The optimal sleeping mechanism can be derived based on the energy-delay tradeoff. This work summarizes the relevant contributions of our group: based on the vacation queueing model, closed-form expressions for average power and mean traffic delay were obtained. The conditions for the energy-delay tradeoff were analyzed. Further, the optimal BS sleeping design and the optimal energy-delay tradeoff were obtained, assuming fixed delays. The influence of the mode-changing energy cost was studied. BS sleeping designs for bursty traffic were analyzed, combined with power matching. The optimal BS sleeping mechanism for bursty traffic was obtained with a partially observable Markov decision process (POMDP).