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Serverless computing is a new way of delivering cloud services that abstracts a user from the burden of managing resources and infrastructure. Although a few serverless simulators have been developed, they do not compare to services that are offered by commercial providers. Developing a comprehensive serverless simulation framework is essential towards evaluating scenarios reliant on the serverless paradigm. This paper focuses on introducing a serverless computing model to the DIScrete event baSed Energy Consumption simulaTor for Clouds and Federations (DISSECT-CF) simulator to enable simulating realistic function-as-a-service (FaaS) solutions. This new model is capable of imitating serverless providers in terms of cost model, associating triggers, extracting performance metrics, and customising the configurations of functions. [ABSTRACT FROM AUTHOR]

Modern Cyber Physical Systems (CPS) applications require hardware capable of optimized performance-per-watt efficency. This is usually obtained through massively parallel accelerators such as the GPU. Recent research is therefore investigating novel designs to optimize GPU energy consumption and performance for various applications in the Internet-of-things, autonomous navigation, and industrial robotics domains. This paper presents a survey of the current state-of-the-art approaches for optimizing GPU performance metrics; we present a complete and up-to-date summary of ideas, mechanisms, and potential improvements for next-generation GPU devices. [ABSTRACT FROM AUTHOR]

Due to various advantages such as energy efficiency, reliability and self-monitoring, the smart grid technology has been emerging as the next-generation intelligent power grid system. Some of the well-known telecommunication, IT and power industries have already adopted smart grid technology along their daily business operations. This paper mainly focuses on the prevention of power outage issue in a smart grid environment. More specifically, consider a utility company which sets a threshold on the total power usage of households from a neighbourhood. Whenever the total power usage from the neighbourhood exceeds the threshold, some of the households need to reduce their energy consumptions, thereby avoiding the possibility of power outage. In the literature, this problem is referred to as threshold-based power usage control (TPUC). In order to solve the TPUC problem, the utility company is required to collect the power usage data of households in a periodic fashion. However, due to privacy reasons, the above problem is not straightforward in a privacy-preserving environment. We emphasise that the existing privacy-preserving TPUC algorithms are either insecure or inefficient. Therefore, this paper proposes two novel secure and distributed TPUC protocols by preserving the privacy of households in a given neighbourhood. Furthermore, we empirically show the practical value of the proposed protocols through various experiments. [ABSTRACT FROM AUTHOR]

Energy optimisation is one of the important issues in the research of wireless sensor networks (WSNs). In the application of monitoring, a large number of sensors are scattered uniformly to cover a collection of points of interest (PoIs) distributed randomly in the monitored area. Since the energy of battery-powered sensor is limited in WSNs, sensors are scheduled to wake up in a large-scale sensor network application. In this paper, we consider how to reduce the energy consumption and prolong the lifetime of WSNs through wake-up scheduling with probabilistic sensing model in the large-scale application of monitoring. To extend the lifetime of sensor network, we need to balance the energy consumption of sensors so that there will not be too much redundant energy in some sensors before the WSN terminates. The detection probability and false alarm probability are taken into consideration to achieve a better performance and reveal the real sensing process which is characterised in the probabilistic sensing model. Data fusion is also introduced to utilise information of sensors so that a PoI in the monitored area may be covered by multiple sensors collaboratively, which will decrease the number of sensors that cover the monitored region. Based on the probabilistic model and data fusion, minimum weight probabilistic coverage problem is formulated in this paper. We also propose a greedy method and modified genetic algorithm based on the greedy method to address the problem. Simulation experiments are conducted to demonstrate the advantages of our proposed algorithms over existing work. [ABSTRACT FROM AUTHOR]

High operating temperatures have been a major problem in embedded systems due to high throughput and compact designs required by modern applications. This paper introduces a novel strategy to subdue these high peak temperatures of MPSoC systems by incorporating task migration and task swapping, to eliminate hot spots and thermal gradients. The work further confirms that the proposed techniques help mitigate power consumption while maintaining a high throughput to satisfy the deadline requirements of the tasks in the system. A heuristic approach is presented that helps mitigate the hot spots and gradients specifically formed from the high-frequency execution of high-priority tasks. In addition, energy-aware task migration reduced the energy consumption in the system. Extensive experimental testing on actual hardware and simulation showed very plausible results to confirm the capability of the presented techniques to reduce peak temperatures along with reduced energy consumption in the system. The presented techniques performed better than many other standard and state-of-the-art published approaches in the literature. [ABSTRACT FROM AUTHOR]

Sensor nodes of a typical wireless sensor network (WSN) are battery driven, so energy conservation is a critical factor for node's life time. Thus optimisation of energy consumption is a major objective in the area of WSNs. One such method is asymmetric communication which uses different channel codes and modulation schemes for downlink (base station (BS) to node link) and uplink (node to BS link). In this paper, a performance analysis of different channel code–modulation pair for energy efficient asymmetric communication is carried out followed by the field programmable gate array implementation of channel codes required at the node. The per information bit node energy for the uplink has been calculated for efficient channel code–modulation pair, for three different channels, viz. additive white gaussian noise, Rayleigh flat-fading and log-normal shadowing channels, resulting in reduction in energy consumption at sensor nodes. [ABSTRACT FROM AUTHOR]

The concept of smart grid (SG) has been introduced to improve the operation of the power systems. In modern structures of power systems, different reasons prompt researchers to suggest integrated analysis of multi-carrier energy systems. Considering synergy effects of the couplings between different energy carriers and utilising intelligent technologies for monitoring and controlling of energy flow may change energy system management in the future. In this paper, we propose a new solution which is entitled ‘smart energy hub’ (SEH) that models a multi-carrier energy system in a SG. SEH solutions allow homeowners to manage their energy consumption to reduce their electricity and gas bill. We present this concept for a residential customer by an ‘energy management system’ which uses reinforcement learning algorithm and Monte Carlo estimation method for finding a near optimal solution. The simulation results show that by using this concept and then applying the algorithm for a residential customer, running costs are reduced up to 40% while keeping the household owner's desired comfort levels. [ABSTRACT FROM PUBLISHER]

Increasing system complexity, energy and device reliability, requirement of modular approach, structured layout, effective spatial reuse of resources, scalability and re-programmability have made network-on-chip (NoC) an obvious interconnection design alternative to the ubiquitous bus based on chip communication architecture in system-on-chip. Designing of a topology and its routing scheme plays a vital role in determining performance of any NoC architecture. In recent years, 3D stacked NoC architecture attracts added interest in NoC design as it offers improved performance and shorter global interconnect. In this paper, we have developed a partially, vertically interconnected 3D topology, namely 3D Recursive Network Topology (3D RNT) and prove that the topology has a Hamiltonian connectedness. We have developed deadlock-free routing algorithm for the 3D RNT topology. Also, we compare the performance of the 3D RNT with partially and fully connected 3D mesh topologies (3D PMT and 3D FMT) by conducting suitable experiments. The experiment results show that there is not much deviation in respect of the performance of the 3D RNT on comparing with 3D PMT and 3D FMT even though a number of vertical links are trimmed down to 75%, which is an encouraging outcome as far as design space is concerned. [ABSTRACT FROM AUTHOR]

Energy efficiency and Quality of Service (QoS) providing are known to be critical design concerns in routing protocols for Wireless Sensor Networks (WSNs). Recent studies, demonstrate that Opportunistic Routing (OR) can greatly improve the performance of WSNs by exploiting the broadcast nature of the wireless medium. In this paper, we propose a new QoS aware and Energy efficient Opportunistic Routing protocol (QEOR) to efficiently routing data under QoS and energy constraints for WSNs. QEOR uses a new multi-metric QoS based candidate selection method in order to accurately select and prioritise the candidate forwarders. The selection is focused on a QoS function that takes into consideration the reliabilty of buffers and links, while the prioritisation is established according to transmission delays. To achieve an obvious improvement on the energy consumption, QEOR uses an energy efficient coordination method and an implicit ACKnowledgement scheme for collision and redundancy avoidance. Simulation results show that QEOR provides best performances as compared to other OR protocols. [ABSTRACT FROM AUTHOR]

Well-known ‘routing hole’ problem of geographic routing is hardly avoided in wireless sensor networks because of various actual geographical environments. Existing geographic routing protocols use perimeter routing strategies to find a detour path around the boundary of holes when they encounter thelocal minimumduring greedy forwarding. However, this solution may lead to uneven energy consumption around the holes since it consumes more energy of the boundary sensors. It becomes more serious when holes appear in most of routing paths in a large-scale sensor network. In this paper, we propose a novel distributed strategy to balance the traffic load on the boundary of holes by virtually changing the sizes of these holes. The proposed mechanism dynamically controls holes to expand and shrink circularly without changing the underlying forwarding strategy. Therefore, it can be applied to most of the existing geographic routing protocols which detour around holes. Simulation results show that our strategy can effectively balance the load around holes, thus prolonging the network life of sensor networks when an existing geographic routing protocol is used as the underlying routing protocol. [ABSTRACT FROM AUTHOR]

Detection of an environmental phenomenon, e.g. air pollution and oil spills, occurs when a group of sensors continuously produces similar readings (i.e. data streams) over a period of time. Thus, detection of environmental phenomena is basically a process of clustering the sensors' data streams, which commonly involves the processing of hundreds and maybe thousands of data streams in real time. Since the sensor network environment is wireless, energy conservation of the sensors would be the main concern. Thus in this paper, we propose an efficient and energy friendly distributed scheme to detect phenomena in a wireless sensor network (WSN). To achieve fast response, the proposed algorithms reduce the dimensionality of the streams. Then, each stream is represented by a point in a multi-dimensional grid. The algorithm uses a grid-based clustering technique to detect clusters of similar stream values. The processing of the algorithm is distributed among different elements of the WSN in a hierarchical topology for more energy efficiency. The paper shows the feasibility of the proposed fully distributed scheme by comparing it with three other WSN schemes in terms of clustering accuracy and energy consumption. [ABSTRACT FROM PUBLISHER]

Wireless microsensor networks usually consist of a large number of small sensor nodes with limited onboard energy supply and deployed densely in a given area for information harvesting purposes. To reduce energy consumption and prolong network lifetime, clustering techniques are often used, among which the grid-based ones are very popular due to their simplicity and scalability. In this paper, we analyse and evaluate the energy-optimal grid size for a grid-based clustering and routing scheme proposed specifically for wireless microsensor networks. In addition, we also consider the effect of data aggregation on energy consumption and network lifetime. Through numerical and simulation results, we reveal the trade-off generic to all grid-based clustering schemes. Further, we propose a randomised technique to prolong the network lifetime and discuss other energy-saving opportunities. This paper provides some insights into the intrinsic limits of grid-based clustering schemes for wireless microsensor networks. [ABSTRACT FROM AUTHOR]

Random walk (RW) based algorithms are often proposed to solve many problems in wireless networks. The implementation of such algorithms are usually done at the application layer so that many characteristics of the wireless transmissions are not exploited. In this paper, we show that we can greatly reduce the energy requirements to perform a RW by better exploiting the broadcast nature of the transmissions. We propose a broadcast based distributed next hop selection algorithm and compare it with a standard unicast based next hop selection algorithm. To evaluate the algorithms, we present a simulation study performed with ns-2. We found that the proposed algorithm is energy efficient, results in less source to destination delay, incurs less number of hops or transmissions and more robust in mobility as compared to a standard unicast based implementation of next hop selection algorithm for RW. [ABSTRACT FROM AUTHOR]

Mobile Ad-hoc Network (MANET) is formed dynamically by the independent organisation of the mobile nodes connected through wireless links without using any centralised administration. Energy consumption is the most significant issue in the MANETs, since most of the mobile hosts operate on limited battery resources. Reduction in the energy consumption results in the increase in the lifetime and throughput of the network. The performance of existing techniques was less in terms of energy conservation issues. To overcome these limitations, this paper proposes an energy conservation mechanism integrated with a proactive MANET routing scheme. The routing schema is based on the energy level and the mobility of the nodes. Calculation of energy cost is performed based on the prediction of the energy consumption level of the node, using Energy Conserving Advanced Optimised Link State Routing (ECAO) model. The performance of the proposed ECAO model is compared with the existing Optimised Link State Routing and Advanced Optimised Link State Routing models. From the comparison result, it is clearly observed that the proposed model achieves better performance in terms of number of TC messages, Packet Delivery Ratio, average time, end-to-end delay, and link delay and energy consumption. [ABSTRACT FROM PUBLISHER]

In this paper, we introduce a new type of sensor: cable sensor. Unlike traditional point sensors, this type of sensor has a rectangular sensing region with a processor installed on it to do processing and communication. The wireless network formed by this kind of sensor is called wireless cable sensor network (WCSN). We study energy-efficient communication algorithms in WCSNs. We address it in two ways: one is through reducing the total transmission power of processors while maintaining the connectivity of the network and the other is through scheduling cable sensors to let them take turns to go to sleep without affecting the coverage and connectivity of the network. In the first approach, we initially develop a distributed algorithm called DTRNG based on the relative neighbourhood graph. Later we enhance it to Algorithm determine the transmission power by removing the largest edge in CYCles (DTCYC). Mathematical proofs show that Algorithm DTCYC provides an optimal solution that can not only minimise the total processor transmission power but maintain the connectivity of the network as well. In the second approach, we propose a cable mode transition algorithm which determines the minimum number of active sensors to maintainK-coverage as well asK-connectivity required by the application. We discuss the relationship between coverage and connectivity and prove the theorems that lay the foundation for our algorithm. Simulation results demonstrate that our algorithm is efficient in saving energy. [ABSTRACT FROM PUBLISHER]

As the number of electric vehicles (EVs) grows, their electricity demands may have significant detrimental impacts on electric power grid when not scheduled properly. In this paper, we model an EV charging system as a cyber-physical system, and design a decentralised online EV charging scheduling algorithm for large populations of EVs, where the EVs can be highly heterogeneous and may join the charging system dynamically. The algorithm couples a clustering-based strategy that dynamically classifies heterogeneous EVs into multiple groups and a sliding-window iterative approach that schedules the charging demand for the EVs in each group in real time. Extensive simulation results demonstrate that our approach provides near-optimal solutions at significantly reduced complexity and communication overhead. It flattens the aggregated load on the power grid and reduces the costs of both the users and the utility. [ABSTRACT FROM PUBLISHER]

Rapid penetration of smart wireless devices and enormous growth of wireless communication technologies have already led to the deployment of wireless sensor networks (WSNs). Sensor nodes in these networks severely suffer from resource constraints, such as energy, buffer, bandwidth and so on. On the other hand, with increasing demand for real-time services in WSN applications, quality of service (QoS)-based routing has emerged as an interesting research topic. Naturally offering some QoS-guarantee in sensor networks raises significant challenges. The networks need to cope up with resource constraints, while providing precise QoS (end-to-end delay and reliability requirement) guarantee. This paper presents QoS aware routing protocol, namely the energy–buffer aware reliable routing (EBARR) protocol that meets both challenges of reliability and timely delivery of critical data. The EBARR achieves reliability by prioritising the packet transmission, reserving the energy and employing efficient buffer management policy to mitigate packet losses due to buffer overflow, hop-by-hop flow control mechanism to reduce packet retransmission overheads along multi-hop routes from source to sink. An angle-based router set ensures the delivery of data within the maximum tolerable time. We evaluate and compare the performance of EBARR protocol with the multi-constraint multi-path routing protocol by means of simulation experiments. Simulation results show that the proposed EBARR protocol efficiently mitigates critical packet losses even under heterogeneous traffic, thereby achieving required reliability and timely delivery of data. [ABSTRACT FROM AUTHOR]

One of the most novel applications of wireless sensor networks in recent years has been in the field of biomedical research. Biomedical sensor networks are formed by tiny wireless sensor nodes, embedded inside the body. The communication protocol used in such networks must prevent the formation of hotspots in the network and at the same time route data efficiently, while conserving energy. In this paper we propose a cross-layer medium access control (MAC) protocol and routing protocol co-design for biomedical sensor networks. The cross-layer interactions among the network and MAC layers help optimise the overall performance of the in vivo network. Extensive simulations have been done to show that the proposed Biocomm protocol performs much better than the other existing MAC and routing protocols in terms of preventing the formation of hotspots, reducing energy consumption of nodes and preventing network congestion when used in an in vivo network. A variation of Biocomm, Biocomm-D has been proposed for delay-sensitive biomedical sensor network applications. [ABSTRACT FROM AUTHOR]

Architecture-based heat dissipation analyses allow us to reveal fundamental sources of inefficiency in a given processor and thereby provide us with road-maps to design less dissipative computing schemes independent of technology-base used to implement them. In this work, we study architectural-level contributions to energy dissipation in an Artificial Neural Network (ANN)-based processor that is trained to perform edge-detection task. We compare the training and information processing cost of ANN to that of conventional architectures and algorithms using 64-pixel binary image. Our results reveal the inherent efficiency advantages of an ANN network trained for specific tasks over general-purpose processors based on von Neumann architecture. We also compare the proposed performance improvements to that of Cellular Array Processors (CAPs) and illustrate the reduction in dissipation for special purpose processors. Lastly, we calculate the change in dissipation as a result of input data structure and show the effect of randomness on energetic cost of information processing. The results we obtained provide a basis for comparison for task-based fundamental energy efficiency analyses for a range of processors and therefore contribute to the study of architecture-level descriptions of processors and thermodynamic cost calculations based on physics of computation. [ABSTRACT FROM AUTHOR]

As an alternative to traditional computing architecture, cloud computing now is rapidly growing. However, it is based on models like cluster computing in general. Now supercomputers are getting more and more powerful, helping scientists have more indepth understanding of the world. At the same time, clusters of commodity servers have been mainstream in the IT industry, powering not only large Internet services but also a growing number of data-intensive scientific applications, such as MPI based deep learning applications. In order to reduce the energy cost, more and more efforts are made to improve the energy consumption of HPC systems. Because I/O accesses account for a large portion of the execution time for data intensive applications, it is critical to design energy-aware parallel I/O functions for addressing challenges related to HPC energy efficiency. As the de facto standard for designing parallel applications in cluster environment, the Message Passing Interface has been widely used in high performance computing, therefore, getting the energy consumption information of MPI applications is critical for improving the energy efficiency of HPC systems. In this work we first present our energy measurement tool, a software framework that eases the energy collection in cluster environment. And then we present an approach which can optimise the parallel I/O operation's energy efficiency. The energy scheduling algorithm is evaluated in a cluster. We try to optimize the energy efficiency of BTIO. Before doing the optimization, we first modeled the execution time and energy consumption. In BTIO, a three-dimensional array is partitioned in a block-tridiagonal pattern and assigned across a square number of processes. Each process is responsible for many (the square root of the number of participating processors) subsets of the entire data set. All parameters of the elemental operations define a multidimensional configuration space. We first modeled the elemental operations. The configuration space of an elemental operation is the cross product of the individual values of the parameters. We sampled points which are evenly distributed in the configuration space, and tested the execution time and consumed energy with MEMT. We divided all the sampled points into training and test set. The majority (75%) of the points were used to iteratively train the execution time and energy models, and the left points were used for testing the models. The models of a collective I/O operation is the combination of these elemental models. The two figures show the modeled versus measured execution time and energy consumption of 250 BTIO operations. We can see that the models behave well. [ABSTRACT FROM AUTHOR]

With recent research in sensor networks and IoT technologies, algorithms for broadcast networks should not only seek to minimise runtime but also energy utilisation. We consider the scenario where processors conserve energy by going into a sleep state for the vast majority of the time. Algorithms under these conditions face additional challenges. Specifically, hibernating processors will not know when tasks that have a random runtime complete. This problem is compounded in recursive algorithms where each task's runtime depends upon the runtime of the recursive calls. We provide a framework, breadth-first recursion, that supports recursive algorithms in this setting. As a simple example, we provide a quick-sort algorithm which is optimal for both time and energy utilisation. We then provide an energy-efficient convex hull algorithm with output sensitive runtime that is sublinear for many realistic distributions for processor placement. Further, we provide simulations that show that our algorithms are practical and significantly faster than previous results. The left side of the image represents our algorithmic technique of breadth first recursion. Inside each box is filled with code and represents the recursive calls of a recursive algorithm. They are arranged in a recursion tree and there is an arrow indicating the order that they are executed. On the right side is a representation of the basic components of our model. They represent low-energy broadcast devices in a single-hop network. [ABSTRACT FROM AUTHOR]

The resource-intensive resettlement procedure and inherent restrictions of the wireless medium obstruct the understanding of faultless implementation in mobile cloud computing (MCC) surroundings. In MCC, transfer of thread execution to high end servers allows the processing of those jobs which demand more resources on the mobile equipment. Hence, implementing the application with stumpy cost, least overhead and non-obtrusive relocation is a demanding research area in MCC. Many scheduling mechanisms have been proposed so far to balance the load between the given set of mobile servers, but it is found to be NP-Hard problem. Therefore, evolutionary techniques are required to balance the load among mobile servers. Genetic Algorithm (GA) has been verified to be fine by jumble up the key values, but it becomes unsuccessful to strengthen the exploration in the rising areas. In Variable Neighborhood Search (VNS), local exploration technique is implemented continually to evaluate optimistic outcomes in neighborhood to attain local best solution. But VNS is still prone to premature convergence traps only because of limited search capability. Therefore hybridization with non-global finding techniques may conquer the limitations and guide the dominant search mechanisms to some extent. The GA along with VNS using thread replication (GVNSTR) is implemented to set stability of non-local searching and local utilization for an evolutionary processing period and get the optimized solution. [ABSTRACT FROM AUTHOR]

In many systems, due to the lack of an adequate positioning capability or the need for energy saving, it is infeasible to track the location of a mobile device as it is moving. Its trajectory, however, may be reconstructed from the real-time fingerprint data that are obtained by the sensors built in the device. For this purpose, we investigate a regularization framework aimed to maximize the localization accuracy by taking into account the spatiotemporal properties regarding the fingerprint space in relation to the location space. The viability of this framework is demonstrated in an evaluation using real-world datasets, which shows its potential to outperform conventional approaches to location fingerprinting. [ABSTRACT FROM PUBLISHER]

Wireless sensor networks often suffer from disrupted connectivity caused by its numerous aspects such as limited battery power of a node and unattended operation vulnerable to hostile tampering. The disruption of connectivity, often referred to asnetwork cut, leads to ill-informed routing decisions, data loss and waste of energy. A number of protocols have been proposed to efficiently detect network cuts; they focus solely on a cut that disconnects nodes from thebase station. However, a cut detection scheme is truly useful when a cut is defined with respect to multiple destinations (i.e. target nodes), rather than a single base station. Thus, we extend the existing notion of cut detection, and propose an algorithm that enables sensor nodes to autonomously monitor the connectivity to multiple target nodes. We introduce a novel reactive cut detection solution, thepoint-to-point cut detection, where given any pair of source and destination, a source is able to locally determine whether the destination is reachable or not. Furthermore, we propose a lightweight proactive cut detection algorithm specifically designed for a network scenario with a small set of target destinations. We prove the effectiveness of the proposed algorithms through extensive simulations; specifically, in our network configurations, proposed cut detection algorithms achieve more than an order of magnitude improvement in energy consumption, when coupled with an underlying routing protocol. [ABSTRACT FROM PUBLISHER]

In relay-based wireless networks, messages need to be forwarded via intermediate relay mobile terminals (MTs). However, because of various transmission distances and unbalanced traffic load, some relay MTs may tend to drain their batteries faster than others. After a certain number of MTs deplete their battery energy, the peer-to-peer communication may become disconnected. Depletion of the battery energy of any relay MT will degrade the performance of the relay-based wireless networks. The network lifetime is defined as the time at which an MT runs out of its battery energy for the first time within the entire network. Moreover, with commercial development of cellular systems proceeding, the research community turns its attention to the next generation systems. It is clear that next generation wireless networks will be heterogeneous wireless networks with a hierarchical overlay of networks of potentially different technologies. However, maintaining quality of service (QoS) in the heterogeneous environments of the future turns out to be a challenging task. In this article, a novel QoS constrained network lifetime extension cellular ad hoc augmented network (QCLE CAHAN) architecture is proposed for next generation wireless networks. The QCLE CAHAN architecture is proposed to achieve the maximum network lifetime under the end-to-end hop-count constraint (QoS constraint). QCLE CAHAN has a hybrid architecture, in which each MT of CDMA cellular networks has ad hoc communication capability. QCLE CAHAN is an evolutionary approach to traditional cellular networks. QCLE CAHAN can dynamically balance battery energy across MTs and extend the network lifetime. QCLE CAHAN can regulate the number of hops between the base station and the MT to adapt to the end-to-end QoS requirements for different services. We show that the network lifetime is much higher in the case of QCLE CAHAN than in the case of traditional cellular networks. [ABSTRACT FROM AUTHOR]

In this article, we study the problem of assigning transmission ranges to the nodes of a wireless sensor network so as to minimize the global power consumption while ensuring a broadcast task. A first novelty is that our model takes into account both the transmission and the reception costs when evaluating the energy consumption. We establish a new analytical model and derive lower and upper bounds on region covering. Moreover, we show that the lower bound is asymptotically optimal and can be approached up to ε. [ABSTRACT FROM AUTHOR]