A case and framework for code analysis–based smartphone application energy estimation

The hype in the popularity of recent wireless technologies has increased applications of smartphones in various fields, particularly, education and health care. The trend of increasing application functionality to enrich smartphone users experience requires detailed insights of application energy consumption behavior. Smartphone application energy estimation helps investigate energy consumption behavior of applications at diversified granularity when it is run on resource‐constrained devices. Fine granular estimation gives more insights to the application energy consumption behavior to assist developers to propose resource‐friendly application designs. This study proposes a lightweight code analysis–based estimation framework to minimize high profiling overhead of use‐based estimation methods. Moreover, it analyzes estimation overhead and accuracy of existing dynamic estimation tools to present a case for code analysis–based energy estimation method. The estimated energy is found 86% accurate to the ground truth value for a set of benchmarks using our proposed framework.     

Analyzing lifetime of energy harvesting underwater wireless sensor nodes

Underwater Wireless Sensor Networks (UWSNs) are utilized to monitor underwater environments that pose many challenges to researchers. One of the key complications of UWSNs is the difficulty of changing node batteries after their energy is depleted. This study aims to diminish the issues related to battery replacement by improving node lifetime. For this goal, three energy harvesting devices (turbine harvester, piezoelectric harvester, and hydrophone harvester) are analyzed to quantitate their impacts on node lifetime. In addition, two different power management schemes (schedule‐driven and event‐driven power management schemes) are combined with energy harvesters for further lifetime improvement. Performance evaluations via simulations show that energy harvesting methods joined by power management schemes can improve node lifetime substantially when actual conditions of Istanbul Bosporus Strait are considered. In this respect, turbine harvester makes the biggest impact and provides lifetime beyond 2000 days for most cases, while piezoelectric harvester can perform the same only for low duty cycle or event arrival values at short transmission ranges.     

Increasing network lifetime by balancing node energy consumption in heterogeneous sensor networks

Sensor nodes are powered by battery and have severe energy constraints. The typical many‐to‐one traffic pattern causes uneven energy consumption among sensor nodes, that is, sensor nodes near the base station or a cluster head have much heavier traffic burden and run out of power much faster than other nodes. The uneven node energy dissipation dramatically reduces sensor network lifetime. In a previous work, we presented the chessboard clustering scheme to increase network lifetime by balancing node energy consumption. To achieve good performance and scalability, we propose to form a heterogeneous sensor network by deploying a few powerful high‐end sensors in addition to a large number of low‐end sensors. In this paper, we design an efficient routing protocol based on the chessboard clustering scheme, and we compute the minimum node density for satisfying a given lifetime constraint. Simulation experiments show that the chessboard clustering‐based routing protocol balances node energy consumption very well and dramatically increases network lifetime, and it performs much better than two other clustering‐based schemes. Copyright © 2006 John Wiley & Sons, Ltd.     

Improving physical layer security via cooperative diversity in energy‐constrained cognitive radio networks with multiple eavesdroppers

In this paper, a scheme that exploits cooperative diversity of multiple relays to provide physical layer security against an eavesdropping attack is concerned. Relay‐based cognitive radio network (CRN) faces issues multiple issues other than the same as faced by conventional wireless communications. If the nodes in a CRN are able to harvest energy and then spend less energy than the total energy available, we can ensure a perpetual lifetime for the network. In this paper, an energy‐constrained CRN is considered where relay nodes are able to harvest energy. A cooperative diversity‐based relay and subchannel‐selection algorithm is proposed, which selects a relay and a subchannel to achieve the maximum secrecy rate while keeping the energy consumed under a certain limit. A transmission power factor is also selected by the algorithm, which ensures long‐term operation of the network. The power allocation problem at the selected relay and at the source also satisfies the maximum‐interference constraint with the primary user (PU). The proposed scheme is compared with a variant of the proposed scheme where the relays are assumed to have an infinite battery capacity (so maximum transmission power is available in every time slot) and is compared with a scheme that uses jamming for physical layer security. The simulation results show that the infinite battery‐capacity scheme outperforms the jamming‐based physical layer security scheme, thus validating that cooperative diversity‐based schemes are suitable to use when channel conditions are better employed, instead of jamming for physical layer security.     

Energy‐efficient resource allocation for lifetime maximization in M2M networks

Machine‐to‐machine (M2M) communications is one of the major enabling technologies for the realization of the Internet of Things (IoT). Most machine‐type communication devices (MTCDs) are battery powered, and the battery lifetime of these devices significantly affects the overall performance of the network and the quality of service (QoS) of the M2M applications. This paper proposes a lifetime‐aware resource allocation algorithm as a convex optimization problem for M2M communications in the uplink of a single carrier frequency division multiple access (SC‐FDMA)‐based heterogeneous network. A K‐means clustering is introduced to reduce energy consumption in the network and mitigate interference from MTCDs in neighbouring clusters. The maximum number of clusters is determined using the elbow method. The lifetime maximization problem is formulated as a joint power and resource block maximization problem, which is then solved using Lagrangian dual method. Finally, numerical simulations in MATLAB are performed to evaluate the performance of the proposed algorithm, and the results are compared to existing heuristic algorithm and inbuilt MATLAB optimal algorithm. The simulation results show that the proposed algorithm outperforms the heuristic algorithm and closely model the optimal algorithm with an acceptable level of complexity. The proposed algorithm offers significant improvements in the energy efficiency and network lifetime, as well as a faster convergence and lower computational complexity.     

Battery-Aware Routing for Streaming Data Transmissions in Wireless Sensor Networks

Recent technological advances have made it possible to support long lifetime and large volume streaming data transmissions in sensor networks. A major challenge is to maximize the lifetime of battery-powered sensors to support such transmissions. Battery, as the power provider of the sensors, therefore emerges as the key factor for achieving high performance in such applications. Recent study in battery technology reveals that the behavior of battery discharging is more complex than we used to think. Battery powered sensors might waste a huge amount of energy if we do not carefully schedule and budget their discharging. In this paper we study the effect of battery behavior on routing for streaming data transmissions in wireless sensor networks. We first give an on-line computable energy model to mathematically model battery discharge behavior. We show that the model can capture and describe battery behavior accurately at low computational complexity and thus is suitable for on-line battery capacity computation. Based on this battery model we then present a battery-aware routing (BAR) protocol to schedule the routing in wireless sensor networks. The routing protocol is sensitive to the battery status of routing nodes and avoids energy loss. We use the battery data from actual sensors to evaluate the performance of our protocol. The results show that the battery-aware protocol proposed in this paper performs well and can save a significant amount of energy compared to existing routing protocols for streaming data transmissions. Network lifetime is also prolonged with maximum data throughput. As far as we know, this is the first work considering battery-awareness with an accurate analytical on-line computable battery model in sensor network routing. We believe the battery model can be used to explore other energy efficient schemes for wireless networks as well.     

A technique to improve network lifetime in mobile ad hoc networks

Energy is an important issue in mobile ad hoc networks (MANETs), and different energy‐aware routing mechanisms have been proposed to minimize the energy consumption in MANETs. Most of the energy‐aware routing schemes reported in the literature have considered only the residual battery capacity as the cost metric in computing a path. In this paper, we have proposed, an energy‐aware routing technique which considers the following parameters: (i) a cost metric, which is a function of residual battery power and energy consumption rate of participating nodes in path computation; (ii) a variable transmission power technique for transmitting data packets; and (iii) To minimize the over‐utilization of participating nodes, a limit is set on the number of paths that can be established to a destination through a participating node. The proposed scheme is simulated using Qualnet 4.5 simulator, and compared with Ad hoc On‐Demand Distance Vector (AODV) and Lifetime Enhancement Routing (LER). We observed that the proposed scheme performs better in terms of network lifetime and energy consumption. Copyright © 2014 John Wiley & Sons, Ltd.     

Lifetime evaluation in energy‐efficient rectangular ad hoc wireless networks

Ad hoc wireless network nodes are typically battery‐powered, therefore energy limit is one of the critical constraints of ad hoc wireless networks' development. This paper evaluates the network lifetime of a rectangular network model that achieves energy efficiency by optimizing the node radio range based on the geographical adaptive fidelity (GAF) topology management protocol (Proceedings of ACMMobil'01, July 2001; 70–84). We derive the optimal transmission range of nodes and analyze both static and dynamic traffic scenarios in both equal‐grid and adjustable‐grid rectangular GAF models, where the results show that the adjustable‐grid model saves 78.1% energy in comparison with the minimum energy consumption of equal‐grid model. The impact of node density on both equal‐grid and adjustable‐grid models is investigated to achieve grid‐lifetime balance among all grids to optimize the entire network lifetime. The lifetime estimation results show that without node density control the adjustable‐grid model prolongs the entire network lifetime by a factor of 4.2 compared with the equal‐grid model. Furthermore, the adjustable‐grid model with node density control is able to prolong the entire network lifetime by a factor of 6 compared with the equal‐grid model. Copyright © 2010 John Wiley & Sons, Ltd.     

Efficient flow‐control algorithm cooperating with energy allocation scheme for solar‐powered WSNs

Recently, solar energy emerged as a feasible supplement to battery power for wireless sensor networks (WSNs) which are expected to operate for long periods. Since solar energy can be harvested periodically and permanently, solar‐powered WSNs can use the energy more efficiently for various network‐wide performances than traditional battery‐based WSNs of which aim is mostly to minimize the energy consumption for extending the network lifetime. However, using solar power in WSNs requires a different energy management from battery‐based WSNs since solar power is a highly varying energy supply. Therefore, firstly we describe a time‐slot‐based energy allocation scheme to use the solar energy optimally, based on expectation model for harvested solar energy. Then, we propose a flow‐control algorithm to maximize the amount of data collected by the network, which cooperates with our energy allocation scheme. Our algorithms run on each node in a distributed manner using only local information of its neighbors, which is a suitable approach for scalable WSNs. We implement indoor and outdoor testbeds of solar‐powered WSN and demonstrate the efficiency of our approaches on them. Copyright © 2010 John Wiley & Sons, Ltd.     

WCDS‐DCR: an energy‐efficient data‐centric routing scheme for wireless sensor networks

Wireless sensor networks (WSNs) typically consist of a large number of battery‐constrained sensors often deployed in harsh environments with little to no human control, thereby necessitating scalable and energy‐efficient techniques. This paper proposes a scalable and energy‐efficient routing scheme, called WCDS‐DCR, suitable for these WSNs. WCDS‐DCR is a fully distributed, data‐centric, routing technique that makes use of an underlying clustering structure induced by the construction of WCDS (Weakly Connected Dominating Set) to prolong network lifetime. It aims at extending network lifetime through the use of data aggregation (based on the elimination of redundant data packets) by some particular nodes. It also utilizes both the energy availability information and the distances (in number of hops) from sensors to the sink in order to make hop‐by‐hop, energy‐aware, routing decisions. Simulation results show that our solution is scalable, and outperforms existing schemes in terms of network lifetime. Copyright © 2010 John Wiley & Sons, Ltd.     

Achieving energy‐neutral data transmission by adjusting transmission power for energy‐harvesting wireless sensor networks

Recently, benefiting from rapid development of energy harvesting technologies, the research trend of wireless sensor networks has shifted from the battery‐powered network to the one that can harvest energy from ambient environments. In such networks, a proper use of harvested energy poses plenty of challenges caused by numerous influence factors and complex application environments. Although numerous works have been based on the energy status of sensor nodes, no work refers to the issue of minimizing the overall data transmission cost by adjusting transmission power of nodes in energy‐harvesting wireless sensor networks. In this paper, we consider the optimization problem of deriving the energy‐neutral minimum cost paths between the source nodes and the sink node. By introducing the concept of energy‐neutral operation, we first propose a polynomial‐time optimal algorithm for finding the optimal path from a single source to the sink by adjusting the transmission powers. Based on the work earlier, another polynomial‐time algorithm is further proposed for finding the approximated optimal paths from multiple sources to the sink node. Also, we analyze the network capacity and present a near‐optimal algorithm based on the Ford–Fulkerson algorithm for approaching the maximum flow in the given network. We have validated our algorithms by various numerical results in terms of path capacity, least energy of nodes, energy ratio, and path cost. Simulation results show that the proposed algorithms achieve significant performance enhancements over existing schemes. Copyright © 2016 John Wiley & Sons, Ltd.     

Instruction‐Level Power Estimator for Sensor Networks

In sensor networks, analyzing power consumption before actual deployment is crucial for maximizing service lifetime. This paper proposes an instruction‐level power estimator (IPEN) for sensor networks. IPEN is an accurate and fine grain power estimation tool, using an instruction‐level simulator. It is independent of the operating system, so many different kinds of sensor node software can be simulated for estimation. We have developed the power model of a Micaz‐compatible mote. The power consumption of the ATmega128L microcontroller is modeled with the base energy cost and the instruction overheads. The CC2420 communication component and other peripherals are modeled according to their operation states. The energy consumption estimation module profiles peripheral accesses and function calls while an application is running. IPEN has shown excellent power estimation accuracy, with less than 5% estimation error compared to real sensor network implementation. With IPEN's high precision instruction‐level energy prediction, users can accurately estimate a sensor network's energy consumption and achieve fine‐grained optimization of their software.     

Cuckoo: flexible compute‐intensive task offloading in mobile cloud computing

Mobile cloud computing (MCC) is an emerging technology to facilitate complex application execution on mobile devices. Mobile users are motivated to implement various tasks using their mobile devices for great flexibility and portability. However, such advantages are challenged by the limited battery life of mobile devices. This paper presents Cuckoo, a scheme of flexible compute‐intensive task offloading in MCC for energy saving. Cuckoo seeks to balance the key design goals: maximize energy saving (technical feasibility) and minimize the impact on user experience with limited cost for offloading (realistic feasibility). Specifically, using a combination of static analysis and dynamic profiling, compute‐intensive tasks are fine‐grained marked from mobile application codes offline. According to the network transmission technologies supported in mobile devices and the runtime network conditions, adopting “task‐bundled” strategy online offloads these tasks to MCC. In the task‐hosted stage, we propose a skyline‐based online resource scheduling strategy to satisfy the realistic feasibility of MCC. In addition, we adopt resource reservation to reduce the extra energy consumption caused by the task multi‐offloading phenomenon. Further, we evaluate the performance of Cuckoo using real‐life data sets on our MCC testbed. Our extensive experiments demonstrate that Cuckoo is able to balance energy consumption and execution performance. Copyright © 2016 John Wiley & Sons, Ltd.     

Energy usage analysis of digital modulations in wireless sensor networks with realistic battery model

A wireless sensor network is a collection of tiny sensor nodes that are deployed to monitor a physical environment. These sensor nodes are generally powered by non-renewable batteries and maybe deployed in harsh environment. Thus, energy resource is precious that makes protocols design for this kind of networks a crucial challenge. Especially, in physical layer, orthogonal modulations as PPM or FSK are suitable. The commonly used models to investigate the network lifetime are based on a linear battery discharge. Really, the battery discharge is closely bonded to the discharge current, and typically is non-linear. This paper presents a performance analysis of both PPM and FSK modulations used in battery powered wireless sensor node. A Rakhmatov–Vrudhula–Wallach model is used to evaluate the used battery charge for a given instantaneous current load. By numerical results, it is proved that PPM modulation outperforms FSK one in term of battery charge use for different network density and for different M-ary signaling schemes.     

Capsule network‐based data pruning in wireless sensor networks

The development of the wireless sensor networks (WSN) being deployed among numerous application for its sensing capabilities is increasing at a very fast tread. Its distributed nature and ability to extend communication even to the inaccessible areas beyond communication range that lacks human intervention has made it even more attractive in a wide space of applications. Confined with numerous sensing nodes distributed over a wide area, the WSN incurs certain limitations as it is battery powered. Many developed routing enhancements with power and energy efficiency lacked in achieving the significant improvement in the performance. So, the paper proposes a machine learning system (capsule network) and technique (data pruning) for WSN involved in the real world observations to have knowledge‐based learning from the experience for an intelligent way of handling the dynamic and real environment without the intervention of the humans. The WSN cluster‐based routing aided with capsule network and data pruning proffered in paper enables the WSN to have a prolonged network lifetime, energy efficiency, minimized delay, and enhanced throughput by reducing the energy usage and extending communication within the limited battery availability. The proposed system is validated in the network simulator and compared with the WSN without ML to check for the performance enhancements of the WSN with ML inclusions in terms of quality of service enhancements, network lifetime, packet delivery ratio, and energy to evince the efficacy of the WSN with capsule network‐based data pruning.     

SPEECH‐MAC: Special purpose energy‐efficient contention‐based hybrid MAC protocol for WSN and Zigbee network

Energy‐efficient Zigbee‐based wireless sensor network (WSN) occupies a major role in emergency‐based applications. The foremost drawback of such applications is maintaining the battery power because frequent changing is not possible in those conditions. In the earlier days, several researches created new model MAC protocols in terms of increase the lifetime of the WSN. But still, there is a research gap particularly in emergency applications. In order to improve the lifetime of such applications, we introduced a novel hybrid MAC protocol, namely, special purpose energy‐efficient contention‐based hybrid MAC (SPEECH‐MAC) protocol. This protocol includes dual hop concept considerably to save the energy. Both the single hop network and the dual hop networks are developed, and the results are analyzed. Prioritization mechanism for SPEECH‐MAC protocol is introduced to analyze the emergency conditions in detail. In summary, according to the simulation, the calculated parameters are total residual energy, end‐to‐end delay, packet drop, packet delivery ratio, and network throughput.     

Framed slotted aloha based MAC protocol for low energy critical infrastructure monitoring networks

Recently, the IEEE TG4k has been formed to amend the IEEE 802.15 family to address the low energy critical infrastructure monitoring networks. The purpose is to facilitate point to multi‐thousands of point communication to collect the scheduled and event data from a large number of nonmains powered endpoints that are widely dispersed. It should support low energy operation, which is necessary for multiyear battery life. Other major features are application data rate up to 40 Kb/s, thousands of endpoints per mains powered infrastructure, asymmetric application data flow, small and infrequent messages, tolerant to data latency, etc. In this paper, we present a discussion on low energy critical infrastructure monitoring networks. We propose a medium access control protocol based on framed slotted aloha for these networks. We investigated probable packet sizes, energy consumptions, battery lifetime and the success rate for our protocol. The proposed protocol is simple to implement. Simulation results show that it is efficient in terms of packet success rate, energy consumption, and battery lifetime.Copyright © 2012 John Wiley & Sons, Ltd.     

Energy efficient scheduling for downlink elastic traffic in wireless networks

In wireless networks, maximizing throughput and minimizing energy consumption are two conflicting objectives. For elastic traffic, it is the total completion time, not the delay constraint of a single packet or the short‐term throughput requirement, that directly affects the quality‐of‐service (QoS). At the same time, the energy consumption should be minimized in order to prolong the battery lifetime of the mobile station (MS). In this paper, we propose energy efficient schedulers that consider throughput and energy saving simultaneously. Through extensive simulations, we compare the proposed schemes with the conventional scheme where a mobile terminal stays awake until all the pending packets are completely serviced. The simulation results show that our schemes outperform the conventional one in terms of utility, i.e., user satisfaction, which is defined as inversely proportional to the multiplication of weighted service completion time and energy consumption. Copyright © 2009 John Wiley & Sons, Ltd.     

A new algorithm for cluster head selection in LEACH protocol for wireless sensor networks

In wireless sensor network, a large number of sensor nodes are distributed to cover a certain area. Sensor node is little in size with restricted processing power, memory, and limited battery life. Because of restricted battery power, wireless sensor network needs to broaden the system lifetime by reducing the energy consumption. A clustering‐based protocols adapt the use of energy by giving a balance to all nodes to become a cluster head. In this paper, we concentrate on a recent hierarchical routing protocols, which are depending on LEACH protocol to enhance its performance and increase the lifetime of wireless sensor network. So our enhanced protocol called Node Ranked–LEACH is proposed. Our proposed protocol improves the total network lifetime based on node rank algorithm. Node rank algorithm depends on both path cost and number of links between nodes to select the cluster head of each cluster. This enhancement reflects the real weight of specific node to success and can be represented as a cluster head. The proposed algorithm overcomes the random process selection, which leads to unexpected fail for some cluster heads in other LEACH versions, and it gives a good performance in the network lifetime and energy consumption comparing with previous version of LEACH protocols.     

Single‐source multibattery solar charger: case study and implementation issues

In this paper, design process and functionality of a portable single‐panel dual‐battery solar charger prototype are presented, achieving energy density of 571 W h kg⁻¹ during a typical 3‐day infantry mission. The device may instantaneously charge up to two Li‐ion MR‐2791 batteries, supporting plug‐and‐play operation. The system consists of a lightweight custom solar panel, based on 20% efficient monocrystalline photovoltaics, and an intelligent power processing module. The panel contains eight transparent polymer‐encapsulated and camouflaged series‐connected six solar cell packs with antiparallel diodes, allowing partial shading operation. The power processing module consists of two synchronous current‐mode‐controlled buck converters, digital signal processor, and a microcontroller, supporting both maximum power point tracking of the solar panel with partial shading detection and multimode charging of Li‐ion packs while instantaneously communicating with the batteries. Power management algorithmic design is presented, based on ensuring system stability while supporting the required operation modes. System implementation stages and underlying issues are thoroughly discussed, and utilized hardware components are presented in detail. Experimental results of system testing under real outdoor conditions are presented to demonstrate the device functionality and energy yield capabilities. Copyright © 2015 John Wiley & Sons, Ltd.