Dynamic Modulation Scaling Research Articles

Dynamic modulation scaling enabled multi-hop topology control for time critical wireless sensor networks

The previous work on connection driven topology control has shown that it has significant potential to reduce energy consumption of Wireless Sensor Networks (WSNs). Dynamic Modulation Scaling (DMS) which is a technique that manages transmission power levels in order to change the number of bits encoded per symbol has a direct impact on connection driven topology control. In this paper we investigate the transmission scheduling of multi-hop real-time WSNs equipped with DMS enabled radio chips while taking the effect of DMS on topology control into account. To our best knowledge, this is the first paper that addresses this issue. The current work on DMS enabled WSN tend to rely on theoretical DMS models to predict network performance metrics. However, there is little, if any, work that is based upon empirically verified network performance outcomes using DMS especially on its effect on connection driven topology control. This paper fills this gap by using GNU Radio and Software Defined Radio hardware to show how to emulate DMS in low power wireless systems and measure the impact of varying Signal-to-Noise levels, distance and elevation on throughput and delivery rates for different DMS control strategies. Next, we present the Mixed Integer Nonlinear Optimization Problem of minimizing energy consumption of DMS enabled connection driven topology control on real-time WSNs. Lastly, we present two polynomial time heuristics and compare their performance against the optimal solution.

DMS-Based Energy Optimizations for Clustered WSNs

In this article, we consider clustered wireless sensor networks where the nodes harvest energy from the environment. We target performance-sensitive applications that have to collectively send their information to a cluster head by a predefined deadline. The nodes are equipped with Dynamic Modulation Scaling (DMS)-capable wireless radios. DMS provides a tuning knob, allowing us to trade off communication latency with energy consumption. We consider two optimization objectives, maximizing total energy reserves and maximizing the minimum energy level across all nodes. For both objectives, we show that optimal solutions can be obtained by solving Mixed Integer Linear Programming problems. We also develop several fast heuristics that are shown to provide approximate solutions experimentally.

On minimizing expected energy usage of embedded wireless systems with probabilistic workloads

A large number of embedded wireless systems must handle complex and time-varying computational and communication workloads. Further, a significant number of these systems support real-time applications. Most of the existing energy management studies for such systems have focused on relatively simple scenarios that assume deterministic workloads, and only consider a limited range of energy management techniques, such as Dynamic Voltage Scaling (DVS). Our paper addresses these deficiencies by proposing a general purpose probabilistic workload model for computation and communication. To account for the importance of radio energy consumption, we also analyse Dynamic Modulation Scaling (DMS), an often overlooked method for energy management. We define several energy control algorithms, including an optimal combined DVS–DMS approach, and evaluate these algorithms under a wide range of workload values and hardware settings. Our results illustrate the benefits of joint power control algorithms.

Joint Scheduling of Tasks and Messages for Energy Minimization in Interference-Aware Real-Time Sensor Networks

Emerging applications of wireless sensor networks mandate extensive in-network information processing and communication while requiring energy efficiency. Dense deployments of wireless nodes and shared wireless channel pose severe interference constraints. Several scheduling schemes in literature propose interference-aware message scheduling with the objective of energy minimization, but the problem of joint scheduling of tasks and messages for energy minimization in interference-aware manner has not been studied. We formulate a Mixed Integer Linear Program (MILP) for the joint scheduling of computation tasks and communication messages in data collection tree based networks. We propose a three phase heuristic which first performs joint scheduling of tasks and messages and then reduces the energy consumption of the network by using the energy saving techniques like Dynamic Voltage Scaling (DVS) for tasks and Dynamic Modulation Scaling (DMS) for messages. These techniques tradeoff energy with latency. However, in dense deployments of WSN with small transmitter receiver distances, DMS does not monotonically reduce the energy consumption. We use this knowledge to efficiently perform slack allocation. We present a Mixed Integer Linear Programming (MILP) formulation to obtain the optimal solution. We evaluate the performance of the proposed algorithm for a variety of scenarios and our results show that the energy savings obtained by the proposed algorithm competes closely with that of the MILP solution.

Energy minimization by exploiting data redundancy in real-time wireless sensor networks

In this paper, we devise scheduling algorithms to minimize energy consumption in Real-time Wireless Sensor Networks (WSNs) by leveraging the energy-delay tradeoff. Spatial and temporal correlation exhibited by WSNs can be exploited to reduce energy consumption. Previous research works attempt to exploit this correlation by way of aggregation or adaptive sampling. We propose determination of the data correlation at each node by way of local computation and propose avoiding transmission of significantly similar data. This can lead to unused time slots at runtime (dynamic slack) which can be traded off for energy savings. Our approach uses techniques namely Dynamic Voltage Scaling (DVS) and Dynamic Modulation Scaling (DMS), which utilize the slack generated dynamically to reduce energy consumption in a real-time environment. We propose heuristics with varying complexities for efficient slack management. We evaluate the performance of these heuristics by simulating diverse network conditions while incorporating different overheads. Our results show that the proposed heuristics can achieve energy savings up to 40% more than the baseline algorithms employing DVS and DMS, and, can achieve performance competitive with a Clairvoyant algorithm under network scenarios with high volume of redundant messages.

The Study on Communication Energy Saving Algorithm of Wireless Sensor Network

Wireless transceiver’s energy consumption takes a high proportion of sensors’ whole energy consumption. Saving wireless transceiver’s energy consumption is significant because of limited energy of wireless sensor network nodes. This paper is based on the model of wireless transceiver’s energy consumption which works in a long distance. Predicted on proving real-time character, this paper poses a long distance low energy consumption scheduling algorithm which adopts dynamic modulation scaling technology combined heuristic algorithm. First, dispatching the modulation level off-line, allocating task and solidifying dispatch result into dispatcher manager. Then, according to the practical transmission time during transmission, adjust the modulation levels again. The simulation experiment proves the algorithm’s effective.

The Study on Communication Energy Saving Algorithm of Wireless Sensor Network

Wireless transceiver’s energy consumption takes a high proportion of sensors’ whole energy consumption. Saving wireless transceiver’s energy consumption is significant because of limited energy of wireless sensor network nodes. This paper is based on the model of wireless transceiver’s energy consumption which works in a long distance. Predicted on proving real-time character, this paper poses a long distance low energy consumption scheduling algorithm which adopts dynamic modulation scaling technology combined heuristic algorithm. First, dispatching the modulation level off-line, allocating task and solidifying dispatch result into dispatcher manager. Then, according to the practical transmission time during transmission, adjust the modulation levels again. The simulation experiment proves the algorithm’s effective.

CAP (Coordinated Adaptive Power) Management Technique with Adaptive Threshold Policy for Wireless Sensor Nodes

Small size of the sensor nodes put constraint on the hardware capabilities and battery supported on it. Though power optimization is a real challenge in all WSN applications, power saving at the cost of data loss is not at all preferable. Very small buffer sizes cannot store the increased data arrival at the time of event occurrences and data gets lost due to buffer overflow. Data loss within the node occurs before transmission mainly either data gets timed out or due to buffer overflows. Both of these situations can be handled using CAP management techniques at individual sensor nodes. Here Dynamic Voltage Frequency Scaling (DVFS) and Dynamic Modulation Scaling (DMS) techniques are used for optimization of power consumed by processor and transmitter respectively. We have considered that DVFS and DMS are coordinated together so that whenever processor changes its processing speed, transmitter also changes its transmission rate and minimum data loss occurs at the intermediate buffer. In this paper we have discussed adaptive threshold policy for selecting a particular state of sensor node and compared the results with fixed threshold policy. CAP management along with adaptive threshold policy improves the lifetime of sensor nodes and also reduces the data loss before transmission.

Optimizing Power and Buffer Congestion on Wireless Sensor Nodes Using CAP (Coordinated Adaptive Power) Management Technique

Limited hardware capabilities and very limited battery power supply are the two main constraints that arise because of small size and low cost of the wireless sensor nodes. Power optimization is highly desired at all the levels in order to have a long lived Wireless Sensor Network (WSN). Prolonging the life span of the network is the prime focus in highly energy constrained wireless sensor networks. Sufficient number of active nodes can only ensure proper coverage of the sensing field and connectivity of the network. If large number of wireless sensor nodes get their batteries depleted over a short time span then it is not possible to maintain the network. In order to have long lived network it is mandatory to have long lived sensor nodes and hence power optimization at node level becomes equally important as power optimization at network level. In this paper need for a dynamically adaptive sensor node is signified in order to optimize power at individual nodes along with the reduction in data loss due to buffer congestion. We have analyzed a sensor node with fixed service rate (processing rate and transmission rate) and a sensor node with variable service rates for its power consumption and data loss in small sized buffers under varying traffic (workload) conditions. For variable processing rate Dynamic Voltage Frequency Scaling (DVFS) technique is considered and for variable transmission rate Dynamic Modulation Scaling (DMS) technique is considered. Comparing the results of a dynamically adaptive sensor node with that of a fixed service rate sensor node shows improvement in the lifetime of node as well as reduction in the data loss due to buffer congestion. Further we have tried to coordinate the service rates of computation unit and communication unit on a sensor node which give rise to Coordinated Adaptive Power (CAP) management. The main objective of CAP Management is to save the power during normal periods and reduce the data loss due to buffer congestion (overflow) during catastrophic periods. With CAP management we are trying to adaptively change the power consumption of sensor nodes. Power consumption of processing unit and communication unit are coordinated together and changed adaptively with respect to the workload. Coordination between processing and communication subunits results in better energy optimization as well as possible data loss before transmission because of limited buffer sizes can be avoided.

Implementation of an energy-efficient scheduling scheme based on pipeline flux leak monitoring networks

Flow against pipeline leakage and the pipe network sudden burst pipe to pipeline leakage flow for the application objects, an energy-efficient real-time scheduling scheme is designed extensively used in pipeline leak monitoring. The proposed scheme can adaptively adjust the network rate in real-time and reduce the cell loss rate, so that it can efficiently avoid the traffic congestion. The recent evolution of wireless sensor networks has yielded a demand to improve energy-efficient scheduling algorithms and energy-efficient medium access protocols. This paper proposes an energy-efficient real-time scheduling scheme that reduces power consumption and network errors on pipeline flux leak monitoring networks. The proposed scheme is based on a dynamic modulation scaling scheme which can scale the number of bits per symbol and a switching scheme which can swap the polling schedule between channels. Built on top of EDF scheduling policy, the proposed scheme enhances the power performance without violating the constraints of real-time streams. The simulation results show that the proposed scheme enhances fault-tolerance and reduces power consumption. Furthermore, that Network congestion avoidance strategy with an energy-efficient real-time scheduling scheme can efficiently improve the bandwidth utilization, TCP friendliness and reduce the packet drop rate in pipeline flux leak monitoring networks.

End-to-End Energy Management in Networked Real-Time Embedded Systems

Performing end-to-end energy management for the data aggregation application poses certain unique challenges particularly when the computational demands on the individual nodes are significant. In this paper, we address the problem of minimizing the total energy consumption of data aggregation with an end-to-end latency constraint while taking into account both the computational and communication workloads in the network. We consider a model where individual nodes support both dynamic voltage scaling (DVS) and dynamic modulation scaling (DMS) power management techniques and explore the energy-time tradeoffs these techniques offer. Specifically, we make the following contributions in this paper. First, we present an analytical problem formulation for the ideal case where each node can scale its frequency and modulation continuously. Second, we prove that the problem is NP-hard for practical scenarios where such continuity cannot be supported. We then present a mixed integer linear programming (MILP) formulation to obtain the optimal solution for the practical problem. Further, we present polynomial time heuristic algorithms which employ the energy-gain metric. We evaluated the performance of the proposed algorithms for a variety of scenarios and our results show that the energy savings obtained by the proposed algorithms are comparable to that of MILP.

An energy-efficient real-time scheduling scheme on dual-channel networks

The recent evolution of wireless sensor networks have yielded a demand to improve energy-efficient scheduling algorithms and energy-efficient medium access protocols. This paper proposes an energy-efficient real-time scheduling scheme that reduces power consumption and network errors on dual channel networks. The proposed scheme is based on a dynamic modulation scaling scheme which can scale the number of bits per symbol and a switching scheme which can swap the polling schedule between channels. Built on top of EDF scheduling policy, the proposed scheme enhances the power performance without violating the constraints of real-time streams. The simulation results show that the proposed scheme enhances fault-tolerance and reduces power consumption.

Energy efficient wireless packet scheduling and fair queuing

As embedded systems are being networked, often wirelessly, an increasingly larger share of their total energy budget is due to the communication. This necessitates the development of power management techniques that address communication subsystems, such as radios, as opposed to computation subsystems, such as embedded processors, to which most of the research effort thus far has been devoted. In this paper, we present techniques for energy efficient packet scheduling and fair queuing in wireless communication systems. Our techniques are based on an extensive slack management approach that dynamically adapts the output rate of the system in accordance with the input packet arrival rate. We use a recently proposed radio power management technique, dynamic modulation scaling (DMS), as a control knob to enable energy-latency trade-offs during wireless packet transmission. We first analyze a single input stream scenario, and describe a rate adaptation technique that results in significantly lower energy consumption (reductions of up to 10 ×), while still bounding the resulting packet delays. By appropriately setting the various parameters of our algorithm, the system can be made to traverse the energy-latency-fidelity trade-off space. We extend our techniques to a multiple input stream scenario, and present E 2 WFQ , an energy efficient version of the weighted fair queuing (WFQ) algorithm for fair packet scheduling. Simulation results show that large energy savings can be obtained through the use of E 2 WFQ , with only a small, bounded increase in worst case packet latency. Further, our results demonstrate that E 2 WFQ does not adversely affect the throughput allocation (and hence, fairness) of WFQ.

Power management for energy-aware communication systems

System-level power management has become a key technique to render modern wireless communication devices economically viable. Despite their relatively large impact on the system energy consumption, power management for radios has been limited to shutdown-based schemes, while processors have benefited from superior techniques based on dynamic voltage scaling (DVS). However, similar scaling approaches that trade-off energy versus performance are also available for radios. To utilize these in radio power management, existing packet scheduling policies have to be thoroughly rethought to make them energy-aware, essentially opening a whole new set of challenges the same way the introduction of DVS did to CPU task scheduling. We use one specific scaling technique, dynamic modulation scaling (DMS), as a vehicle to outline these challenges, and to introduce the intricacies caused by the nonpreemptive nature of packet scheduling and the time-varying wireless channel.