1,720,966 research outputs found
Quality of service aware cross-layer network lifetime maximization in battery-constrained wireless sensor networks
No full textIn wireless sensor networks (WSNs), network lifetime (NL) maximization plays a significant role in striking a compelling compromise between maximizing the overall throughput and minimizing the energy dissipation, while extending the duration of adequate communications without battery-replacement, when the sensor nodes rely on limited energy supply. Hence, this thesis focuses on the NL maximization of battery-constrained WSNs, which is vitally important in industrial applications, where thousands of sensors may be deployed within a specific target area and the energy dissipation of each sensor node has to be minimized in order to reduce the overall cost of the applications to the industry. However, maintaining stringent quality of service (QoS) requirements under the above-mentioned NL constraints can be challenging and requires careful consideration of several conflicting design tradeoffs. Naturally, the above-mentioned energy dissipation characteristics are dependent on the entire seven-layer OSI protocol stack, where each layer contributes to the dissipated energy. Therefore, NL maximization necessitates a cross-layer operation across all these layers, where each layer has to minimize its energy dissipation without deteriorating the QoS. Hence, our objective is to maximize the NL using cross-layer design techniques in the interest of maintaining certain QoS requirements and to provide the system designer with well-informed decisions prior to embarking on hardware implementations. Hence, our approach is to investigate and to model progressively more realistic WSNs.We commence with a broad overview of the WSNs, of the design objectives and of the NL maximization techniques that have been investigated in the literature. We then provide a concise introduction to convexity, convex optimization, to the Lagrangian dual problem and to the Karush-Kuhn-Tucker (KKT) optimality conditions, which will be extensively used in our studies. Having presented the fundamentals, we formulate an initial study of the NL maximization problem based on a simple string topology in order to form a basic framework for the NL maximization of more realistic large scale networks. In this particular study, we maximize the NL in an interference-limited WSN considering a beneficial rate and power allocation scheme under both additive white Gaussian noise (AWGN) and fading channel characteristics, where we employ the KKT optimality conditions for obtaining the optimal solution to the NL maximization problem using closed-form expressions. Therefore, we were able to derive analytical expressions of the globally optimal NL for a string network operating in an interference-limited scenario, while communicating either over an AWGN or over fading channels for a given link schedule. Furthermore, the maximum NL, the energy dissipation per node, the average transmission power per link and the lifetime of all nodes in the network are obtained. We quantify how the maximum NL is reduced as a function of the fading statistics due to the poor channel conditions. Furthermore, we demonstrate that given a certain network-sum-rate, the simultaneous scheduling of weakly interfering links benefits from the associated spatial reuse by allowing each node to transmit at a lower rate, which requires a reduced transmission power and hence results in an increased NL. We also conclude that the choice of the particular scheduling scheme depends on the application, since a lower source rate favors infrequent transmissions requiring a low transmit power, while avoiding the detrimental effects of interference, when aiming for extending the NL. However, we observe that for higher source rates, a higher NL can be achieved by aggressive spatial reuse. An interesting observation is that increasing the distance between the consecutive nodes substantially reduces the NL, especially for lower source rates. However, quite surprisingly, increasing the distance between the consecutive nodes results in an improved NL for higher source rates. This is due to the reduced impact of the interferers located at a higher distance. More explicitly, even though the transmit power required has to be increased to satisfy the rate constraint, at the same time the interferers are moved a bit further away. In this particular study, the NL and source rate are considered as the QoS measure as a function of both the transmit rate and the power, where an adaptive scheme is assumed. Finally, our proposed algorithm achieved reduced complexity NL maximization compared to other techniques found in the literature.We then extend our NL maximization problem to a realistic scenario, where the parameters are selected from the practical data sheet of a National Instruments device, which is based on the IEEE 802.15.4 Standard and the energy dissipation of the signal processing operations, i.e. the energy dissipation of the transceiver circuits, is considered. Since achieving a reasonable NL at the cost of a tolerable end-to-end bit error rate (BER) for a fixed-rate system using various modulation and coding schemes (MCSs) is an important objective for the system designer considering the QoS, we strike a trade-off between the BER and the NL, which is crucial for network designers at an early design stage. Therefore, we aim for maximizing the NL for a predetermined set of target signal-to-interference-plus-noise ratio (SINR) values, which guarantees maintaining the predefined QoS of each link operating over either an AWGN channel or a Rayleigh block-fading channel, while considering or disregarding the signal processing power (SPP). We observed that especially for low target SINRs, the SPP has a dominant impact on the NL. However, for higher target SINRs the achievable NL only considering the transmit power whilst disregarding the SPP forms a benchmark for the achievable NL of the particular scenario, when the SPP is jointly considered along with the transmit power.As a further advance, a more realistic network is considered, where the same National Instruments device, which is based on the IEEE 802.15.4 Standard is used as a reference. For this realistic network, we also had to reconsider our NL definition, where we maximize the NL of a WSN relying on randomly and uniformly distributed fully connected nodes. This fully connected WSN imposes an exponentially increasing routing complexity upon increasing the number of nodes. More particularly, we focus our attention on the crosslayer optimization of the power allocation, scheduling and routing operations for the sake of NL maximization for predetermined per-link target SINR values. We use the so-called exhaustive search algorithm (ESA) as our benchmarker and conceive a near-optimal single objective genetic algorithm (SOGA) imposing a substantially reduced complexity in fully connected WSNs. We show that our NL maximization approach is powerful in terms of prolonging the NL, while striking a trade-off between the NL and the QoS requirements. Finally, we consider a multiobjective NL maximization problem, where the end-to-end delay and the energy dissipation are considered as our conflicting design objectives. More explicitly, we proposed a novel NL optimization design in order to reflect the effect of the end-to-end delay on the NL along with the aggregate energy dissipation of the same route. The distinctive aspect of this study is the simultaneous optimization of both the aggregate energy dissipation and of the end-to-end delay as a multi-objective optimization problem in order to provide the system designer with a trade-off between Pareto-optimal energyand delay-solutions. We employ multi-objective evolutionary algorithms (MOEAs), namely the so-called non-dominated sorting based genetic algorithm-II (NSGA-II) and the multiobjective differential evolution (MODE) algorithm for obtaining the set of Pareto optimal NL-aware routes striking a trade-off between the aggregate energy dissipation and the end-to-end delay. Moreover, we characterize both the complexity and the convergence of both algorithms compared to the ESA
Route lifetime analysis for vehicular networks
No full textVehicular communications have a critical role in the future intelligent transportation systems as they promise safer, more efficient and enjoyable driving. Multi-hop communications can be employed through routing protocols in order to achieve increased data transmission and coverage. However, most vehicular applications encounter significant challenges due to the increased mobility of the vehicles. This leads to transient communication links, which may significantly degrade the overall routing performance. Additionally, efficient routing techniques are required in order to achieve a reliable and flexible vehicular communications system. In this paper, we provide a framework for route lifetime analysis based on an exhaustive search routing technique to set an upper bound on the lifetime, which can inform the design of routing techniques. We then highlight the purpose of our framework by comparing the upper bound to a shortest-path forwarding mechanism based on global state routing (GSR), chosen as a routing example. The route lifetime is measured in order to reveal the trade-off between the structure and dimension of the road-network and performance requirements as a fundamental research baseline for investigating and developing sophisticated routing models for vehicular networks. Our results reveal that, on average, the shortest-path route lifetime is sub-optimal 11.56% of the time compared to the upper bound route lifetime. Then, we provide a trade-off between the road-network dimensions and the quality-of-service (QoS) requirements, and finally demonstrate that on average, under channel fading conditions, 7 times higher route lifetime could be achieved in a line-of-sight (LOS) scenario compared to its non-line-of-sight (NLOS) counterpart
Multi-source multi-destination hybrid infrastructure-aided traffic aware routing in V2V/I networks
No full textThe concept of the “connected car” offers the potential for safer, more enjoyable and more efficient driving and eventually autonomous driving. However, in urban Vehicular Networks (VNs) , the high mobility of vehicles along roads poses major challenges to the routing protocols needed for a reliable and flexible vehicular communications system. Thus, urban VNs rely on static Road-Side-Units (RSUs) to forward data and to extend coverage across the network. In this paper, we first propose a new Q-learning-based routing algorithm, namely Infrastructure-aided Traffic-Aware Routing (I-TAR), which leverages the static wired RSU infrastructure for packet forwarding. Then, we focus on the multi-source, multi-destination problem and the effect this imposes on node availability, as nodes also participate in other communications paths. This motivates our new hybrid approach, namely Hybrid Infrastructure-aided Traffic Aware Routing (HITAR)that aims to select the best Vehicle-to-Vehicle/Infrastructure (V2V/I) route. Our findings demonstrate that I-TAR can achieve up to 19% higher average packet-delivery-ratio (APDR) compared to the state-of-the-art. Under a more realistic scenario, where node availability is considered, a decline of up to 51% in APDR performance is observed, whereas the proposed HI-TAR in turn can increase the APDR performance by up to 50% compared to both I-TAR and the state-of-the-art. Finally, when multiple source destination vehicle pairs are considered, all the schemes that model and consider node availability, i.e. limited-availability, achieve from 72.2% to 82.3% lower APDR, when compared to those that do not, i.e. assuming full-availability. However, HI-TAR still provides 34.6% better APDR performance than I-TAR, and ∼40% more than the state-of-the-art
Multi-objective routing optimization using evolutionary algorithms
Full text linkWireless ad hoc networks suffer from several limitations, such as routing failures, potentially excessive bandwidth requirements, computational constraints and limited storage capability. Their routing strategy plays a significant role in determining the overall performance of the multi-hop network. However, in conventional network design only one of the desired routing-related objectives is optimized, while other objectives are typically assumed to be the constraints imposed on the problem. In this paper, we invoke the Non-dominated Sorting based Genetic Algorithm-II (NSGA-II) and the MultiObjective Differential Evolution (MODE) algorithm for finding optimal routes from a given source to a given destination in the face of conflicting design objectives, such as the dissipated energy and the end-to-end delay in a fully-connected arbitrary multi-hop network. Our simulation results show that both the NSGA-II and MODE algorithms are efficient in solving these routing problems and are capable of finding the Pareto-optimal solutions at lower complexity than the ’brute-force’ exhaustive search, when the number of nodes is higher than or equal to 10. Additionally, we demonstrate that at the same complexity, the MODE algorithm is capable of finding solutions closer to the Pareto front and typically, converges faster than the NSGA-II algorithm
Cross-layer network lifetime maximization in interference-limited WSNs
Full text linkIn wireless sensor networks (WSNs), the network lifetime (NL) is a crucial metric since the sensor nodes usually rely on limited energy supply. In this paper, we consider the joint optimal design of the physical, medium access control (MAC), and network layers to maximize the NL of the energy-constrained WSN. The problem of NL maximization can be formulated as a nonlinear optimization problem encompassing the routing flow, link scheduling, transmission rate, and power allocation operations for all active time slots (TSs). The resultant nonconvex rate constraint is relaxed by employing an approximation of the signal-to-interference-plus-noise ratio (SINR), which transforms the problem to a convex one. Hence, the resultant dual problem may be solved to obtain the optimal solution to the relaxed problem with a zero duality gap. Therefore, the problem is formulated in its Lagrangian form, and the Karush–Kuhn–Tucker (KKT) optimality conditions are employed for deriving analytical expressions of the globally optimal transmission rate and power allocation variables for the network topology considered. The nonlinear Gauss–Seidel algorithm is adopted for iteratively updating the rate and power allocation variables using these expressions until convergence is attained. Furthermore, the gradient method is applied for updating the dual variables in each iteration. Using this approach, the maximum NL, the energy dissipation per node, the average transmission power per link, and the lifetime of all nodes in the network are evaluated for a given source rate and fixed link schedule under different channel conditions
Network-lifetime maximization of wireless sensor networks
No full textNetwork lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance for extending the duration of the operations in the battery-constrained wireless sensor networks (WSNs). In this paper, we consider a two-stage NL maximization technique conceived for a fully-connected WSN, where the NL is strictly dependent on the source node’s (SN) battery level, since we can transmit information generated at the SN to the destination node (DN) via alternative routes, each having a specific route lifetime (RL) value. During the first stage the RL of the alternative routes spanning from SN to DN is evaluated, where the RL is defined as the earliest time, at which a sensor node lying in the route fully drains its battery charge. The second stage involves the summation of these RL values, until the SN’s battery is fully depleted, which constitutes the lifetime of the WSN considered. Each alternative route is evaluated using cross-layer optimization of the power allocation, scheduling and routing operations for the sake of NL maximization for a predetermined per-link target signal-to-interference-plus-noise ratio (SINR) values. Therefore, we propose the optimal but excessive-complexity algorithm, namely the exhaustive search algorithm (ESA) and a near-optimal single objective genetic algorithm (SOGA) exhibiting a reduced complexity in a fully connected WSN. We demonstrate that in a high-complexity WSN, the SOGA is capable of approaching the ESA’s NL within a tiny margin of 3.02% at a 2.56 times reduced complexity. We also show that our NL maximization approach is powerful in terms of prolonging the NL, while striking a trade-off between the NL and the quality of service (QoS) requirements
Cross-layer network lifetime optimization considering transmit and signal processing power in WSNs
No full textMaintaining high energy efficiency is essential for increasing the lifetime of wireless sensor networks (WSNs), where the battery of the sensor nodes cannot be routinely replaced. Nevertheless, the energy budget of the WSN strictly relies on the communication parameters, where the choice of both the transmit power as well as of the modulation and coding schemes (MCSs) plays a significant role in maximizing the network lifetime (NL). In this paper, we optimize the NL of WNSs by analysing the impact of the physical layer parameters as well as of the signal processing power (SPP) P_sp on the NL. We characterize the underlying trade-offs between the NL and bit error ratio (BER) performance for a predetermined set of target signal-to-interference-plus-noise ratio (SINR) values and for different MCSs using periodic transmit-time slot (TS) scheduling in interference-limited WSNs. For a per-link target BER requirement (PLBR) of 10^?3, our results demonstrate that a ’continuous-time’ NL in the range of 0.58?4.99 years is achieved depending on the MCSs, channel configurations, and SPP
A survey of network lifetime maximization techniques in wireless sensor networks
No full textEmerging technologies, such as the Internet of things, smart applications, smart grids and machine-to-machine networks stimulate the deployment of autonomous, selfconfiguring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteri
Twin-Component Near-Pareto Routing Optimization for AANETs in the North-Atlantic Region Relying on Real Flight Statistics
Full text linkIntegrated ground-air-space (IGAS) networks intrinsically amalgamate terrestrial and non-terrestrial communication techniques in support of universal connectivity across the globe. Multi-hop routing over the IGAS networks has the potential to provide long-distance highly directional connections in the sky. For meeting the latency and reliability requirements of in-flight connectivity, we formulate a multi-objective multi-hop routing problem in aeronautical ad hoc networks (AANETs) for concurrently optimizing multiple end-to-end performance metrics
in terms of the total delay and the throughput. In contrast to single-objective optimization problems that may have a unique optimal solution, the problem formulated is a multi-objective combinatorial optimization problem (MOCOP), which generally has a set of trade-off solutions, called the Pareto optimal set. Due to the discrete structure of the MOCOP formulated, finding the Pareto optimal set becomes excessively complex for large-scale networks. Therefore, we employ a multi-objective evolutionary algorithm (MOEA), namely the classic NSGA-II for generating an approximation of the Pareto optimal set. Explicitly, with the intrinsic parallelism of MOEAs, the MOEA employed starts with a set of candidate solutions for creating and reproducing new solutions via genetic operators. Finally, we evaluate the MOCOP
formulated for different networks generated both from simulated
data as well as from real historical flight data. Our simulation results demonstrate that the utilized MOEA has the potential of finding the Pareto optimal solutions for small-scale networks, while also finding a set of high-performance nondominated solutions for large-scale networks
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