Multi-hop Cooperative Relaying for Energy Efficient In Vivo Communications

This paper investigates cooperative relaying to support energy efficient in vivo communications. In such a network, the in vivo source nodes transmit their sensing information to an on-body destination node either via direct communications or by employing on-body cooperative relay nodes in order to promote energy efficiency. Two relay modes are investigated, namely single-hop and multi-hop (two-hop) relaying. In this context, the paper objective is to select the optimal transmission mode (direct, single-hop, or two-hop relaying) and relay assignment (if cooperative relaying is adopted) for each source node that results in the minimum per bit average energy consumption for the in vivo network. The problem is formulated as a binary program that can be efficiently solved using commercial optimization solvers. Numerical results demonstrate the significant improvement in energy consumption and quality-of-service (QoS) support when multi-hop communication is adopted

A new energy consumption technique for mobile ad hoc networks

A dynamic temporary network is created through wireless mobile nodes without the need for considerable infrastructure as well as a central manager. In a mobile ad hoc network, routing protocols allow a mobile for transmission and receiving packets. In the last decade, many variants have come up for the AODV. A minimum number of hop counts are chosen for enhancing routing protocols to include additional factors that can have an impact on path selections. As the distance between each node grows, the transmission power also rises accordingly. Hence, this impacts the network’s entire performance and the most important feature is the quality of service. Most of the traditional routing protocols include energy consumption levels of the nodes and various parameters, like residual battery power, consumption of energy per packet and energy needed per transmission. A new technique is proposed in this paper to enhance the routing efficiency by making use of lion optimization algorithm after identifying all possible paths in the network. This technique not only enhances the energy efficiency of each node but also the performance metrics

Ultra Dense Small Cell Networks: Turning Density into Energy Efficiency

In this paper, a novel approach for joint power control and user scheduling is proposed for optimizing energy efficiency (EE), in terms of bits per unit energy, in ultra dense small cell networks (UDNs). Due to severe coupling in interference, this problem is formulated as a dynamic stochastic game (DSG) between small cell base stations (SBSs). This game enables to capture the dynamics of both the queues and channel states of the system. To solve this game, assuming a large homogeneous UDN deployment, the problem is cast as a mean-field game (MFG) in which the MFG equilibrium is analyzed with the aid of low-complexity tractable partial differential equations. Exploiting the stochastic nature of the problem, user scheduling is formulated as a stochastic optimization problem and solved using the drift plus penalty (DPP) approach in the framework of Lyapunov optimization. Remarkably, it is shown that by weaving notions from Lyapunov optimization and mean-field theory, the proposed solution yields an equilibrium control policy per SBS which maximizes the network utility while ensuring users' quality-of-service. Simulation results show that the proposed approach achieves up to 70.7% gains in EE and 99.5% reductions in the network's outage probabilities compared to a baseline model which focuses on improving EE while attempting to satisfy the users' instantaneous quality-of-service requirements.Comment: 15 pages, 21 figures (sub-figures are counted separately), IEEE Journal on Selected Areas in Communications - Series on Green Communications and Networking (Issue 2

Wireless sensor networks in motion : clustering algorithms for service discovery and provisioning

The evolution of computer technology follows a trajectory of miniaturization\ud and diversification. The technology has developed from mainframes (large computers used by many people) to personal computers (one computer per person)\ud and recently, embedded computers (many computers per person). One of the\ud smallest embedded computers is a wireless sensor node, which is a batterypowered\ud miniaturized device equipped with processing capabilities, memory,\ud wireless communication and sensors that can sense the physical parameters of\ud the environment. A collection of sensor nodes that communicate through the\ud wireless interface form a Wireless Sensor Network (WSN), which is an ad-hoc,\ud self organizing network that can function unattended for long periods of time.\ud Although traditionally WSNs have been regarded as static sensor arrays\ud used mainly for environmental monitoring, recently, WSN applications have\ud undergone a paradigm shift from static to more dynamic environments, where\ud nodes are attached to moving objects, people or animals. Applications that\ud use WSNs in motion are broad, ranging from transport and logistics to animal\ud monitoring, health care and military, just to mention a few.\ud These application domains have a number of characteristics that challenge\ud the algorithmic design of WSNs. Firstly, mobility has a negative effect on\ud the quality of the wireless communication and the performance of networking\ud protocols. Nevertheless, it has been shown that mobility can enhance the functionality of the network by exploiting the movement patterns of mobile objects. Secondly, the heterogeneity of devices in a WSN has to be taken into account for increasing the network performance and lifetime. Thirdly, the WSN services should ideally assist the user in an unobtrusive and transparent way. Fourthly, energy-efficiency and scalability are of primary importance to prevent the network performance degradation. This thesis focuses on the problems and enhancements brought in by networ mobility, while also accounting for heterogeneity, transparency, energy efficiency and scalability. We propose a set of algorithms that enable WSNs to self-organize efficiently in the presence of mobility, adapt to and even exploit dynamics to increase the functionality of the network. Our contributions include an algorithm for motion detection, a set of clustering algorithms that can be used to handle mobility efficiently, and a service discovery protocol that enables dynamic user access to the WSN functionality

Electromagnetic emission-aware schedulers for the uplink of OFDM wireless communication systems

The popularity and convergence of wireless communications have resulted in continuous network upgrades in order to support the increasing demand for bandwidth. However, given that wireless communication systems operate on radiofrequency waves, the health effects of electromagnetic emission from these systems are increasingly becoming a concern due to the ubiquity of mobile communication devices. In order to address these concerns, we propose two schemes (offline and online) for minimizing the EM emission of users in the uplink of OFDM systems, while maintaining an acceptable quality of service. We formulate our offline EM reduction scheme as a convex optimization problem and solve it through water-filling. This is based on the assumption that the long-term channel state information of all the users is known. Given that, in practice, long-term channel state information of all the users cannot always be available, we propose our online EM emission reduction scheme, which is based on minimizing the instantaneous transmit energy per bit of each user. Simulation results show that both our proposed schemes significantly minimize the EM emission when compared to the benchmark classic greedy spectral efficiency based scheme and an energy efficiency based scheme. Furthermore, our offline scheme proves to be very robust against channel prediction errors

Energy-Efficient Resource Allocation in Wireless Networks: An Overview of Game-Theoretic Approaches

An overview of game-theoretic approaches to energy-efficient resource allocation in wireless networks is presented. Focusing on multiple-access networks, it is demonstrated that game theory can be used as an effective tool to study resource allocation in wireless networks with quality-of-service (QoS) constraints. A family of non-cooperative (distributed) games is presented in which each user seeks to choose a strategy that maximizes its own utility while satisfying its QoS requirements. The utility function considered here measures the number of reliable bits that are transmitted per joule of energy consumed and, hence, is particulary suitable for energy-constrained networks. The actions available to each user in trying to maximize its own utility are at least the choice of the transmit power and, depending on the situation, the user may also be able to choose its transmission rate, modulation, packet size, multiuser receiver, multi-antenna processing algorithm, or carrier allocation strategy. The best-response strategy and Nash equilibrium for each game is presented. Using this game-theoretic framework, the effects of power control, rate control, modulation, temporal and spatial signal processing, carrier allocation strategy and delay QoS constraints on energy efficiency and network capacity are quantified.Comment: To appear in the IEEE Signal Processing Magazine: Special Issue on Resource-Constrained Signal Processing, Communications and Networking, May 200

Energy-Efficient Signalling in QoS Constrained Heterogeneous Networks

© 2013 IEEE. This paper considers a heterogeneous network, which consists of one macro base station and numerous small cell base stations (SBSs) cooperatively serving multiple user terminals. The first objective is to design cooperative transmit beamformers at the base stations to maximize the network energy efficiency (EE) in terms of bits per joule subject to the users' quality of service (QoS) constraints, which poses a computationally difficult optimization problem. The commonly used Dinkelbach-type algorithms for optimizing a ratio of concave and convex functions are not applicable. This paper develops a path-following algorithm to address the computational solution to this problem, which invokes only a simple convex quadratic program of moderate dimension at each iteration and quickly converges at least to a locally optimal solution. Furthermore, the problem of joint beamformer design and SBS service assignment in the three-objective (EE, QoS, and service loading) optimization is also addressed. Numerical results demonstrate the performance advantage of the proposed solutions

Energy-Efficient Resource Management in Ultra Dense Small Cell Networks: A Mean-Field Approach

In this paper, a novel approach for joint power control and user scheduling is proposed for optimizing energy efficiency (EE), in terms of bits per unit power, in ultra dense small cell networks (UDNs). To address this problem, a dynamic stochastic game (DSG) is formulated between small cell base stations (SBSs). This game enables to capture the dynamics of both queues and channel states of the system. To solve this game, assuming a large homogeneous UDN deployment, the problem is cast as a mean field game (MFG) in which the MFG equilibrium is analyzed with the aid of two low-complexity tractable partial differential equations. User scheduling is formulated as a stochastic optimization problem and solved using the drift plus penalty (DPP) approach in the framework of Lyapunov optimization. Remarkably, it is shown that by weaving notions from Lyapunov optimization and mean field theory, the proposed solution yields an equilibrium control policy per SBS which maximizes the network utility while ensuring users' quality-of-service. Simulation results show that the proposed approach achieves up to 18:1% gains in EE and 98.2% reductions in the network's outage probability compared to a baseline model.Comment: 6 pages, 7 figures, GLOBECOM 2015 (published